Overview

Brought to you by YData

Dataset statistics

Number of variables223
Number of observations338094
Missing cells50607329
Missing cells (%)67.1%
Total size in memory575.2 MiB
Average record size in memory1.7 KiB

Variable types

Numeric28
Unsupported76
Text111
Boolean8

Dataset

DescriptionNMNH Material Samples (USNM) 0049394-241126133413365
URLhttps://doi.org/10.15468/dl.ycwxgd

Alerts

license has constant value "CC0_1_0" Constant
publisher has constant value "National Museum of Natural History, Smithsonian Institution" Constant
institutionID has constant value "http://grbio.org/cool/142r-0w94" Constant
datasetName has constant value "NMNH Material Samples (USNM)" Constant
basisOfRecord has constant value "MATERIAL_SAMPLE" Constant
occurrenceStatus has constant value "PRESENT" Constant
organismName has constant value "EML" Constant
organismScope has constant value "2024-12-01T12:07:33.811Z" Constant
associatedOrganisms has constant value "2024-12-01T11:07:21.711Z" Constant
previousIdentifications has constant value "True" Constant
materialEntityRemarks has constant value "False" Constant
parentEventID has constant value "Panama" Constant
eventType has constant value "PAN.5_1" Constant
eventTime has constant value "Pinogana" Constant
identifiedByID has constant value "ACCEPTED" Constant
identificationVerificationStatus has constant value "26098c25-8f7f-4c71-97ac-1d3db181c65e" Constant
identificationRemarks has constant value "US" Constant
acceptedNameUsage has constant value "False" Constant
subtribe has constant value "EML" Constant
subgenus has constant value "True" Constant
protocol has constant value "EML" Constant
lastCrawled has constant value "2024-12-01T11:07:21.711Z" Constant
publishedByGbifRegion has constant value "NORTH_AMERICA" Constant
hasGeospatialIssues is highly imbalanced (93.6%) Imbalance
accessRights has 338094 (100.0%) missing values Missing
bibliographicCitation has 338094 (100.0%) missing values Missing
language has 338094 (100.0%) missing values Missing
references has 338094 (100.0%) missing values Missing
rightsHolder has 338094 (100.0%) missing values Missing
type has 338094 (100.0%) missing values Missing
datasetID has 338094 (100.0%) missing values Missing
ownerInstitutionCode has 338094 (100.0%) missing values Missing
informationWithheld has 338094 (100.0%) missing values Missing
dataGeneralizations has 338094 (100.0%) missing values Missing
dynamicProperties has 338094 (100.0%) missing values Missing
catalogNumber has 70677 (20.9%) missing values Missing
recordNumber has 181582 (53.7%) missing values Missing
recordedBy has 70120 (20.7%) missing values Missing
recordedByID has 338094 (100.0%) missing values Missing
individualCount has 39347 (11.6%) missing values Missing
organismQuantity has 338094 (100.0%) missing values Missing
organismQuantityType has 338094 (100.0%) missing values Missing
sex has 265741 (78.6%) missing values Missing
lifeStage has 209004 (61.8%) missing values Missing
reproductiveCondition has 338094 (100.0%) missing values Missing
caste has 338094 (100.0%) missing values Missing
behavior has 338094 (100.0%) missing values Missing
vitality has 338094 (100.0%) missing values Missing
establishmentMeans has 338094 (100.0%) missing values Missing
degreeOfEstablishment has 338094 (100.0%) missing values Missing
pathway has 338094 (100.0%) missing values Missing
georeferenceVerificationStatus has 338094 (100.0%) missing values Missing
preparations has 251111 (74.3%) missing values Missing
associatedOccurrences has 338094 (100.0%) missing values Missing
associatedReferences has 338094 (100.0%) missing values Missing
associatedSequences has 305424 (90.3%) missing values Missing
associatedTaxa has 338094 (100.0%) missing values Missing
otherCatalogNumbers has 338094 (100.0%) missing values Missing
occurrenceRemarks has 193547 (57.2%) missing values Missing
organismID has 338094 (100.0%) missing values Missing
organismName has 338093 (> 99.9%) missing values Missing
organismScope has 338093 (> 99.9%) missing values Missing
associatedOrganisms has 338093 (> 99.9%) missing values Missing
previousIdentifications has 338093 (> 99.9%) missing values Missing
organismRemarks has 338094 (100.0%) missing values Missing
materialEntityID has 338094 (100.0%) missing values Missing
materialEntityRemarks has 338093 (> 99.9%) missing values Missing
verbatimLabel has 338089 (> 99.9%) missing values Missing
materialSampleID has 84986 (25.1%) missing values Missing
eventID has 338092 (> 99.9%) missing values Missing
parentEventID has 338093 (> 99.9%) missing values Missing
eventType has 338093 (> 99.9%) missing values Missing
fieldNumber has 267153 (79.0%) missing values Missing
eventDate has 16903 (5.0%) missing values Missing
eventTime has 338093 (> 99.9%) missing values Missing
startDayOfYear has 19910 (5.9%) missing values Missing
endDayOfYear has 19910 (5.9%) missing values Missing
year has 17140 (5.1%) missing values Missing
month has 22792 (6.7%) missing values Missing
day has 52010 (15.4%) missing values Missing
verbatimEventDate has 235843 (69.8%) missing values Missing
habitat has 302025 (89.3%) missing values Missing
samplingProtocol has 338094 (100.0%) missing values Missing
sampleSizeValue has 338094 (100.0%) missing values Missing
sampleSizeUnit has 338094 (100.0%) missing values Missing
samplingEffort has 338094 (100.0%) missing values Missing
fieldNotes has 338094 (100.0%) missing values Missing
eventRemarks has 338094 (100.0%) missing values Missing
locationID has 284620 (84.2%) missing values Missing
higherGeographyID has 338094 (100.0%) missing values Missing
higherGeography has 4531 (1.3%) missing values Missing
continent has 57738 (17.1%) missing values Missing
waterBody has 231346 (68.4%) missing values Missing
islandGroup has 315374 (93.3%) missing values Missing
island has 279260 (82.6%) missing values Missing
countryCode has 11127 (3.3%) missing values Missing
stateProvince has 66137 (19.6%) missing values Missing
county has 140475 (41.5%) missing values Missing
municipality has 338094 (100.0%) missing values Missing
locality has 34045 (10.1%) missing values Missing
verbatimLocality has 338094 (100.0%) missing values Missing
verbatimElevation has 322170 (95.3%) missing values Missing
verticalDatum has 338094 (100.0%) missing values Missing
verbatimDepth has 336615 (99.6%) missing values Missing
minimumDistanceAboveSurfaceInMeters has 338092 (> 99.9%) missing values Missing
maximumDistanceAboveSurfaceInMeters has 338094 (100.0%) missing values Missing
locationAccordingTo has 338094 (100.0%) missing values Missing
locationRemarks has 338094 (100.0%) missing values Missing
decimalLatitude has 73462 (21.7%) missing values Missing
decimalLongitude has 73462 (21.7%) missing values Missing
coordinateUncertaintyInMeters has 327083 (96.7%) missing values Missing
coordinatePrecision has 338094 (100.0%) missing values Missing
pointRadiusSpatialFit has 338090 (> 99.9%) missing values Missing
verbatimCoordinateSystem has 329029 (97.3%) missing values Missing
verbatimSRS has 338094 (100.0%) missing values Missing
footprintWKT has 338094 (100.0%) missing values Missing
footprintSRS has 338094 (100.0%) missing values Missing
footprintSpatialFit has 338094 (100.0%) missing values Missing
georeferencedBy has 338090 (> 99.9%) missing values Missing
georeferencedDate has 338094 (100.0%) missing values Missing
georeferenceProtocol has 255273 (75.5%) missing values Missing
georeferenceSources has 338094 (100.0%) missing values Missing
georeferenceRemarks has 328595 (97.2%) missing values Missing
geologicalContextID has 338094 (100.0%) missing values Missing
earliestEonOrLowestEonothem has 338094 (100.0%) missing values Missing
latestEonOrHighestEonothem has 338090 (> 99.9%) missing values Missing
earliestEraOrLowestErathem has 338090 (> 99.9%) missing values Missing
latestEraOrHighestErathem has 338090 (> 99.9%) missing values Missing
earliestPeriodOrLowestSystem has 338091 (> 99.9%) missing values Missing
latestPeriodOrHighestSystem has 338091 (> 99.9%) missing values Missing
earliestEpochOrLowestSeries has 338094 (100.0%) missing values Missing
latestEpochOrHighestSeries has 338090 (> 99.9%) missing values Missing
earliestAgeOrLowestStage has 338094 (100.0%) missing values Missing
latestAgeOrHighestStage has 338094 (100.0%) missing values Missing
lowestBiostratigraphicZone has 338094 (100.0%) missing values Missing
highestBiostratigraphicZone has 338090 (> 99.9%) missing values Missing
lithostratigraphicTerms has 338090 (> 99.9%) missing values Missing
group has 338094 (100.0%) missing values Missing
formation has 338094 (100.0%) missing values Missing
member has 338091 (> 99.9%) missing values Missing
bed has 338094 (100.0%) missing values Missing
identificationID has 338094 (100.0%) missing values Missing
verbatimIdentification has 338090 (> 99.9%) missing values Missing
identificationQualifier has 333028 (98.5%) missing values Missing
typeStatus has 331537 (98.1%) missing values Missing
identifiedBy has 226045 (66.9%) missing values Missing
identifiedByID has 338090 (> 99.9%) missing values Missing
dateIdentified has 338094 (100.0%) missing values Missing
identificationReferences has 338094 (100.0%) missing values Missing
identificationVerificationStatus has 338090 (> 99.9%) missing values Missing
identificationRemarks has 338090 (> 99.9%) missing values Missing
taxonID has 338090 (> 99.9%) missing values Missing
scientificNameID has 338092 (> 99.9%) missing values Missing
acceptedNameUsageID has 6111 (1.8%) missing values Missing
parentNameUsageID has 338094 (100.0%) missing values Missing
originalNameUsageID has 338094 (100.0%) missing values Missing
nameAccordingToID has 338094 (100.0%) missing values Missing
namePublishedInID has 338090 (> 99.9%) missing values Missing
taxonConceptID has 338094 (100.0%) missing values Missing
acceptedNameUsage has 338090 (> 99.9%) missing values Missing
parentNameUsage has 338090 (> 99.9%) missing values Missing
originalNameUsage has 338090 (> 99.9%) missing values Missing
nameAccordingTo has 338090 (> 99.9%) missing values Missing
namePublishedIn has 338090 (> 99.9%) missing values Missing
namePublishedInYear has 338091 (> 99.9%) missing values Missing
higherClassification has 5891 (1.7%) missing values Missing
phylum has 6808 (2.0%) missing values Missing
class has 52277 (15.5%) missing values Missing
order has 30344 (9.0%) missing values Missing
superfamily has 338091 (> 99.9%) missing values Missing
family has 19906 (5.9%) missing values Missing
subfamily has 338090 (> 99.9%) missing values Missing
tribe has 338094 (100.0%) missing values Missing
subtribe has 338090 (> 99.9%) missing values Missing
genus has 34392 (10.2%) missing values Missing
genericName has 34393 (10.2%) missing values Missing
subgenus has 338090 (> 99.9%) missing values Missing
infragenericEpithet has 338094 (100.0%) missing values Missing
specificEpithet has 89523 (26.5%) missing values Missing
infraspecificEpithet has 328999 (97.3%) missing values Missing
cultivarEpithet has 338090 (> 99.9%) missing values Missing
verbatimTaxonRank has 338090 (> 99.9%) missing values Missing
vernacularName has 338090 (> 99.9%) missing values Missing
nomenclaturalCode has 338090 (> 99.9%) missing values Missing
taxonomicStatus has 6108 (1.8%) missing values Missing
nomenclaturalStatus has 338091 (> 99.9%) missing values Missing
taxonRemarks has 338091 (> 99.9%) missing values Missing
elevation has 248950 (73.6%) missing values Missing
elevationAccuracy has 284393 (84.1%) missing values Missing
depth has 262666 (77.7%) missing values Missing
depthAccuracy has 272182 (80.5%) missing values Missing
distanceFromCentroidInMeters has 335404 (99.2%) missing values Missing
issue has 45626 (13.5%) missing values Missing
mediaType has 324090 (95.9%) missing values Missing
acceptedTaxonKey has 6112 (1.8%) missing values Missing
phylumKey has 6812 (2.0%) missing values Missing
classKey has 52277 (15.5%) missing values Missing
orderKey has 30347 (9.0%) missing values Missing
familyKey has 19910 (5.9%) missing values Missing
genusKey has 34396 (10.2%) missing values Missing
subgenusKey has 338094 (100.0%) missing values Missing
speciesKey has 89520 (26.5%) missing values Missing
species has 89520 (26.5%) missing values Missing
acceptedScientificName has 6112 (1.8%) missing values Missing
verbatimScientificName has 24039 (7.1%) missing values Missing
typifiedName has 338060 (> 99.9%) missing values Missing
repatriated has 10837 (3.2%) missing values Missing
relativeOrganismQuantity has 338094 (100.0%) missing values Missing
projectId has 338094 (100.0%) missing values Missing
gbifRegion has 12220 (3.6%) missing values Missing
level0Gid has 157741 (46.7%) missing values Missing
level0Name has 157741 (46.7%) missing values Missing
level1Gid has 158980 (47.0%) missing values Missing
level1Name has 158980 (47.0%) missing values Missing
level2Gid has 167237 (49.5%) missing values Missing
level2Name has 167249 (49.5%) missing values Missing
level3Gid has 300258 (88.8%) missing values Missing
level3Name has 300562 (88.9%) missing values Missing
iucnRedListCategory has 63456 (18.8%) missing values Missing
individualCount is highly skewed (γ1 = 36.58205118) Skewed
gbifID has unique values Unique
occurrenceID has unique values Unique
accessRights is an unsupported type, check if it needs cleaning or further analysis Unsupported
bibliographicCitation is an unsupported type, check if it needs cleaning or further analysis Unsupported
language is an unsupported type, check if it needs cleaning or further analysis Unsupported
references is an unsupported type, check if it needs cleaning or further analysis Unsupported
rightsHolder is an unsupported type, check if it needs cleaning or further analysis Unsupported
type is an unsupported type, check if it needs cleaning or further analysis Unsupported
datasetID is an unsupported type, check if it needs cleaning or further analysis Unsupported
ownerInstitutionCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
informationWithheld is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataGeneralizations is an unsupported type, check if it needs cleaning or further analysis Unsupported
dynamicProperties is an unsupported type, check if it needs cleaning or further analysis Unsupported
recordedByID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantityType is an unsupported type, check if it needs cleaning or further analysis Unsupported
reproductiveCondition is an unsupported type, check if it needs cleaning or further analysis Unsupported
caste is an unsupported type, check if it needs cleaning or further analysis Unsupported
behavior is an unsupported type, check if it needs cleaning or further analysis Unsupported
vitality is an unsupported type, check if it needs cleaning or further analysis Unsupported
establishmentMeans is an unsupported type, check if it needs cleaning or further analysis Unsupported
degreeOfEstablishment is an unsupported type, check if it needs cleaning or further analysis Unsupported
pathway is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOccurrences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedTaxa is an unsupported type, check if it needs cleaning or further analysis Unsupported
otherCatalogNumbers is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityID is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLabel is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
startDayOfYear is an unsupported type, check if it needs cleaning or further analysis Unsupported
endDayOfYear is an unsupported type, check if it needs cleaning or further analysis Unsupported
year is an unsupported type, check if it needs cleaning or further analysis Unsupported
day is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingProtocol is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeValue is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeUnit is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingEffort is an unsupported type, check if it needs cleaning or further analysis Unsupported
fieldNotes is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
higherGeographyID is an unsupported type, check if it needs cleaning or further analysis Unsupported
municipality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLocality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verticalDatum is an unsupported type, check if it needs cleaning or further analysis Unsupported
maximumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
coordinatePrecision is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimSRS is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintWKT is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintSRS is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintSpatialFit is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferencedDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceSources is an unsupported type, check if it needs cleaning or further analysis Unsupported
geologicalContextID is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEonOrLowestEonothem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEpochOrLowestSeries is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestAgeOrLowestStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestAgeOrHighestStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
lowestBiostratigraphicZone is an unsupported type, check if it needs cleaning or further analysis Unsupported
group is an unsupported type, check if it needs cleaning or further analysis Unsupported
formation is an unsupported type, check if it needs cleaning or further analysis Unsupported
bed is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationID is an unsupported type, check if it needs cleaning or further analysis Unsupported
dateIdentified is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
originalNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingToID is an unsupported type, check if it needs cleaning or further analysis Unsupported
taxonConceptID is an unsupported type, check if it needs cleaning or further analysis Unsupported
tribe is an unsupported type, check if it needs cleaning or further analysis Unsupported
infragenericEpithet is an unsupported type, check if it needs cleaning or further analysis Unsupported
subgenusKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
relativeOrganismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
projectId is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevationAccuracy has 47745 (14.1%) zeros Zeros
depthAccuracy has 26153 (7.7%) zeros Zeros
taxonKey has 6107 (1.8%) zeros Zeros
kingdomKey has 6107 (1.8%) zeros Zeros

Reproduction

Analysis started2025-01-07 17:09:32.477864
Analysis finished2025-01-07 17:09:49.413580
Duration16.94 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

gbifID
Real number (ℝ)

Unique 

Distinct338094
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3632296955
Minimum3027958301
Maximum4975247457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:49.722089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3027958301
5-th percentile3027975448
Q13028044591
median3028133440
Q34527656522
95-th percentile4948163957
Maximum4975247457
Range1947289156
Interquartile range (IQR)1499611930

Descriptive statistics

Standard deviation810773260.6
Coefficient of variation (CV)0.2232122733
Kurtosis-1.215729352
Mean3632296955
Median Absolute Deviation (MAD)167938
Skewness0.8052595094
Sum1.228057807 × 1015
Variance6.573532801 × 1017
MonotonicityNot monotonic
2025-01-07T12:09:49.809689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4909546919 1
 
< 0.1%
4501677301 1
 
< 0.1%
3027962301 1
 
< 0.1%
3028050301 1
 
< 0.1%
3027962302 1
 
< 0.1%
3028050302 1
 
< 0.1%
4909491303 1
 
< 0.1%
3041539301 1
 
< 0.1%
3357130301 1
 
< 0.1%
3027962303 1
 
< 0.1%
Other values (338084) 338084
> 99.9%
ValueCountFrequency (%)
3027958301 1
< 0.1%
3027958302 1
< 0.1%
3027958303 1
< 0.1%
3027958304 1
< 0.1%
3027958305 1
< 0.1%
ValueCountFrequency (%)
4975247457 1
< 0.1%
4975247456 1
< 0.1%
4975247455 1
< 0.1%
4975247454 1
< 0.1%
4975247453 1
< 0.1%

accessRights
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

bibliographicCitation
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

language
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:49.897343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2366658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC0_1_0
2nd rowCC0_1_0
3rd rowCC0_1_0
4th rowCC0_1_0
5th rowCC0_1_0
ValueCountFrequency (%)
cc0_1_0 338094
100.0%
2025-01-07T12:09:50.147083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 676188
28.6%
0 676188
28.6%
_ 676188
28.6%
1 338094
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2366658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 676188
28.6%
0 676188
28.6%
_ 676188
28.6%
1 338094
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2366658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 676188
28.6%
0 676188
28.6%
_ 676188
28.6%
1 338094
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2366658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 676188
28.6%
0 676188
28.6%
_ 676188
28.6%
1 338094
14.3%
Distinct10795
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:50.269734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters6761880
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2110 ?
Unique (%)0.6%

Sample

1st row2024-06-26T12:37:00Z
2nd row2021-10-14T09:12:00Z
3rd row2022-07-20T16:25:00Z
4th row2021-10-13T15:49:00Z
5th row2019-06-25T16:21:00Z
ValueCountFrequency (%)
2023-06-13t09:52:00z 2840
 
0.8%
2024-10-17t11:06:00z 2662
 
0.8%
2021-10-13t15:50:00z 2652
 
0.8%
2021-10-13t15:49:00z 2517
 
0.7%
2022-10-17t16:14:00z 2414
 
0.7%
2022-10-17t16:13:00z 2368
 
0.7%
2021-10-14t09:09:00z 2340
 
0.7%
2021-10-14t09:10:00z 2235
 
0.7%
2021-10-14t09:08:00z 2151
 
0.6%
2021-10-13t15:48:00z 2042
 
0.6%
Other values (10785) 313873
92.8%
2025-01-07T12:09:50.456096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1616584
23.9%
2 1048026
15.5%
1 802624
11.9%
- 676188
10.0%
: 676188
10.0%
T 338094
 
5.0%
Z 338094
 
5.0%
3 230104
 
3.4%
4 213568
 
3.2%
5 202272
 
3.0%
Other values (4) 620138
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6761880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1616584
23.9%
2 1048026
15.5%
1 802624
11.9%
- 676188
10.0%
: 676188
10.0%
T 338094
 
5.0%
Z 338094
 
5.0%
3 230104
 
3.4%
4 213568
 
3.2%
5 202272
 
3.0%
Other values (4) 620138
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6761880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1616584
23.9%
2 1048026
15.5%
1 802624
11.9%
- 676188
10.0%
: 676188
10.0%
T 338094
 
5.0%
Z 338094
 
5.0%
3 230104
 
3.4%
4 213568
 
3.2%
5 202272
 
3.0%
Other values (4) 620138
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6761880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1616584
23.9%
2 1048026
15.5%
1 802624
11.9%
- 676188
10.0%
: 676188
10.0%
T 338094
 
5.0%
Z 338094
 
5.0%
3 230104
 
3.4%
4 213568
 
3.2%
5 202272
 
3.0%
Other values (4) 620138
 
9.2%

publisher
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:50.527954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters19947546
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Museum of Natural History, Smithsonian Institution
2nd rowNational Museum of Natural History, Smithsonian Institution
3rd rowNational Museum of Natural History, Smithsonian Institution
4th rowNational Museum of Natural History, Smithsonian Institution
5th rowNational Museum of Natural History, Smithsonian Institution
ValueCountFrequency (%)
national 338094
14.3%
museum 338094
14.3%
of 338094
14.3%
natural 338094
14.3%
history 338094
14.3%
smithsonian 338094
14.3%
institution 338094
14.3%
2025-01-07T12:09:50.646448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2366658
11.9%
i 2028564
10.2%
2028564
10.2%
o 1690470
 
8.5%
a 1690470
 
8.5%
n 1690470
 
8.5%
s 1352376
 
6.8%
u 1352376
 
6.8%
N 676188
 
3.4%
m 676188
 
3.4%
Other values (11) 4395222
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19947546
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2366658
11.9%
i 2028564
10.2%
2028564
10.2%
o 1690470
 
8.5%
a 1690470
 
8.5%
n 1690470
 
8.5%
s 1352376
 
6.8%
u 1352376
 
6.8%
N 676188
 
3.4%
m 676188
 
3.4%
Other values (11) 4395222
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19947546
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2366658
11.9%
i 2028564
10.2%
2028564
10.2%
o 1690470
 
8.5%
a 1690470
 
8.5%
n 1690470
 
8.5%
s 1352376
 
6.8%
u 1352376
 
6.8%
N 676188
 
3.4%
m 676188
 
3.4%
Other values (11) 4395222
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19947546
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2366658
11.9%
i 2028564
10.2%
2028564
10.2%
o 1690470
 
8.5%
a 1690470
 
8.5%
n 1690470
 
8.5%
s 1352376
 
6.8%
u 1352376
 
6.8%
N 676188
 
3.4%
m 676188
 
3.4%
Other values (11) 4395222
22.0%

references
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

rightsHolder
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

type
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:50.710800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters10480914
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://grbio.org/cool/142r-0w94
2nd rowhttp://grbio.org/cool/142r-0w94
3rd rowhttp://grbio.org/cool/142r-0w94
4th rowhttp://grbio.org/cool/142r-0w94
5th rowhttp://grbio.org/cool/142r-0w94
ValueCountFrequency (%)
http://grbio.org/cool/142r-0w94 338094
100.0%
2025-01-07T12:09:50.822302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1352376
 
12.9%
/ 1352376
 
12.9%
r 1014282
 
9.7%
t 676188
 
6.5%
4 676188
 
6.5%
g 676188
 
6.5%
h 338094
 
3.2%
p 338094
 
3.2%
: 338094
 
3.2%
b 338094
 
3.2%
Other values (10) 3380940
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10480914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1352376
 
12.9%
/ 1352376
 
12.9%
r 1014282
 
9.7%
t 676188
 
6.5%
4 676188
 
6.5%
g 676188
 
6.5%
h 338094
 
3.2%
p 338094
 
3.2%
: 338094
 
3.2%
b 338094
 
3.2%
Other values (10) 3380940
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10480914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1352376
 
12.9%
/ 1352376
 
12.9%
r 1014282
 
9.7%
t 676188
 
6.5%
4 676188
 
6.5%
g 676188
 
6.5%
h 338094
 
3.2%
p 338094
 
3.2%
: 338094
 
3.2%
b 338094
 
3.2%
Other values (10) 3380940
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10480914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1352376
 
12.9%
/ 1352376
 
12.9%
r 1014282
 
9.7%
t 676188
 
6.5%
4 676188
 
6.5%
g 676188
 
6.5%
h 338094
 
3.2%
p 338094
 
3.2%
: 338094
 
3.2%
b 338094
 
3.2%
Other values (10) 3380940
32.3%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:50.888384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters15214230
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad
2nd rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
3rd rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
4th rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
5th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
ValueCountFrequency (%)
urn:uuid:18e3cd08-a962-4f0a-b72c-9a0b3600c5ad 119032
35.2%
urn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6 74362
22.0%
urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8 42251
 
12.5%
urn:uuid:09c9cf5f-f5d3-48cc-b5c8-cd9b9fbd631f 41564
 
12.3%
urn:uuid:cc104cbf-fd8e-4801-9b71-36731a7db1a0 28248
 
8.4%
urn:uuid:59e56a59-8615-4e0c-841d-eb88f3876b22 24486
 
7.2%
urn:uuid:73d83e23-1999-42cd-b38a-c06a7d32d893 8151
 
2.4%
2025-01-07T12:09:51.035578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1352376
 
8.9%
d 1154135
 
7.6%
c 1039600
 
6.8%
u 1014282
 
6.7%
8 916661
 
6.0%
0 796356
 
5.2%
a 774527
 
5.1%
1 740909
 
4.9%
9 705119
 
4.6%
: 676188
 
4.4%
Other values (12) 6044077
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15214230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1352376
 
8.9%
d 1154135
 
7.6%
c 1039600
 
6.8%
u 1014282
 
6.7%
8 916661
 
6.0%
0 796356
 
5.2%
a 774527
 
5.1%
1 740909
 
4.9%
9 705119
 
4.6%
: 676188
 
4.4%
Other values (12) 6044077
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15214230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1352376
 
8.9%
d 1154135
 
7.6%
c 1039600
 
6.8%
u 1014282
 
6.7%
8 916661
 
6.0%
0 796356
 
5.2%
a 774527
 
5.1%
1 740909
 
4.9%
9 705119
 
4.6%
: 676188
 
4.4%
Other values (12) 6044077
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15214230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1352376
 
8.9%
d 1154135
 
7.6%
c 1039600
 
6.8%
u 1014282
 
6.7%
8 916661
 
6.0%
0 796356
 
5.2%
a 774527
 
5.1%
1 740909
 
4.9%
9 705119
 
4.6%
: 676188
 
4.4%
Other values (12) 6044077
39.7%

datasetID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:51.081596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.750063592
Min length2

Characters and Unicode

Total characters1267874
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSNM
2nd rowUSNM
3rd rowUSNM
4th rowUSNM
5th rowUS
ValueCountFrequency (%)
usnm 295843
87.5%
us 42251
 
12.5%
2025-01-07T12:09:51.197770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 338094
26.7%
S 338094
26.7%
N 295843
23.3%
M 295843
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1267874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 338094
26.7%
S 338094
26.7%
N 295843
23.3%
M 295843
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1267874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 338094
26.7%
S 338094
26.7%
N 295843
23.3%
M 295843
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1267874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 338094
26.7%
S 338094
26.7%
N 295843
23.3%
M 295843
23.3%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:51.245393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.982215005
Min length2

Characters and Unicode

Total characters1008269
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENT
2nd rowIZ
3rd rowIZ
4th rowIZ
5th rowUS
ValueCountFrequency (%)
ent 119032
35.2%
iz 74362
22.0%
us 42251
 
12.5%
fish 41564
 
12.3%
herp 28248
 
8.4%
mamm 24486
 
7.2%
birds 8151
 
2.4%
2025-01-07T12:09:51.355481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 147280
14.6%
I 124077
12.3%
N 119032
11.8%
T 119032
11.8%
S 91966
9.1%
Z 74362
7.4%
M 73458
7.3%
H 69812
6.9%
U 42251
 
4.2%
F 41564
 
4.1%
Other values (5) 105435
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1008269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 147280
14.6%
I 124077
12.3%
N 119032
11.8%
T 119032
11.8%
S 91966
9.1%
Z 74362
7.4%
M 73458
7.3%
H 69812
6.9%
U 42251
 
4.2%
F 41564
 
4.1%
Other values (5) 105435
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1008269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 147280
14.6%
I 124077
12.3%
N 119032
11.8%
T 119032
11.8%
S 91966
9.1%
Z 74362
7.4%
M 73458
7.3%
H 69812
6.9%
U 42251
 
4.2%
F 41564
 
4.1%
Other values (5) 105435
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1008269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 147280
14.6%
I 124077
12.3%
N 119032
11.8%
T 119032
11.8%
S 91966
9.1%
Z 74362
7.4%
M 73458
7.3%
H 69812
6.9%
U 42251
 
4.2%
F 41564
 
4.1%
Other values (5) 105435
10.5%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:51.403296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters9466632
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Material Samples (USNM)
2nd rowNMNH Material Samples (USNM)
3rd rowNMNH Material Samples (USNM)
4th rowNMNH Material Samples (USNM)
5th rowNMNH Material Samples (USNM)
ValueCountFrequency (%)
nmnh 338094
25.0%
material 338094
25.0%
samples 338094
25.0%
usnm 338094
25.0%
2025-01-07T12:09:51.511711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1014282
10.7%
M 1014282
10.7%
1014282
10.7%
a 1014282
10.7%
e 676188
 
7.1%
S 676188
 
7.1%
l 676188
 
7.1%
H 338094
 
3.6%
t 338094
 
3.6%
i 338094
 
3.6%
Other values (7) 2366658
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9466632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1014282
10.7%
M 1014282
10.7%
1014282
10.7%
a 1014282
10.7%
e 676188
 
7.1%
S 676188
 
7.1%
l 676188
 
7.1%
H 338094
 
3.6%
t 338094
 
3.6%
i 338094
 
3.6%
Other values (7) 2366658
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9466632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1014282
10.7%
M 1014282
10.7%
1014282
10.7%
a 1014282
10.7%
e 676188
 
7.1%
S 676188
 
7.1%
l 676188
 
7.1%
H 338094
 
3.6%
t 338094
 
3.6%
i 338094
 
3.6%
Other values (7) 2366658
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9466632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1014282
10.7%
M 1014282
10.7%
1014282
10.7%
a 1014282
10.7%
e 676188
 
7.1%
S 676188
 
7.1%
l 676188
 
7.1%
H 338094
 
3.6%
t 338094
 
3.6%
i 338094
 
3.6%
Other values (7) 2366658
25.0%

ownerInstitutionCode
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

basisOfRecord
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:51.563857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters5071410
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMATERIAL_SAMPLE
2nd rowMATERIAL_SAMPLE
3rd rowMATERIAL_SAMPLE
4th rowMATERIAL_SAMPLE
5th rowMATERIAL_SAMPLE
ValueCountFrequency (%)
material_sample 338094
100.0%
2025-01-07T12:09:51.679974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1014282
20.0%
M 676188
13.3%
E 676188
13.3%
L 676188
13.3%
R 338094
 
6.7%
T 338094
 
6.7%
I 338094
 
6.7%
_ 338094
 
6.7%
S 338094
 
6.7%
P 338094
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5071410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1014282
20.0%
M 676188
13.3%
E 676188
13.3%
L 676188
13.3%
R 338094
 
6.7%
T 338094
 
6.7%
I 338094
 
6.7%
_ 338094
 
6.7%
S 338094
 
6.7%
P 338094
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5071410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1014282
20.0%
M 676188
13.3%
E 676188
13.3%
L 676188
13.3%
R 338094
 
6.7%
T 338094
 
6.7%
I 338094
 
6.7%
_ 338094
 
6.7%
S 338094
 
6.7%
P 338094
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5071410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1014282
20.0%
M 676188
13.3%
E 676188
13.3%
L 676188
13.3%
R 338094
 
6.7%
T 338094
 
6.7%
I 338094
 
6.7%
_ 338094
 
6.7%
S 338094
 
6.7%
P 338094
 
6.7%

informationWithheld
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

dataGeneralizations
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

dynamicProperties
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

occurrenceID
Text

Unique 

Distinct338094
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:51.896844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters21299922
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338094 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/300028c5f-ea1d-4c01-9253-09524fc57db6
2nd rowhttp://n2t.net/ark:/65665/30006cd83-36b3-4629-86db-f5a28307189f
3rd rowhttp://n2t.net/ark:/65665/30007a443-7a0a-49a9-9c54-cae1342160a6
4th rowhttp://n2t.net/ark:/65665/300098b69-426b-451c-a675-27a1b7bb5b60
5th rowhttp://n2t.net/ark:/65665/3000a9424-501b-43e7-a337-ee632a8fa9d0
ValueCountFrequency (%)
http://n2t.net/ark:/65665/3001b5554-545c-479e-a09c-f732f7e77413 1
 
< 0.1%
http://n2t.net/ark:/65665/3fff3e72f-6f91-4423-ba1f-74cf0bea2264 1
 
< 0.1%
http://n2t.net/ark:/65665/300028c5f-ea1d-4c01-9253-09524fc57db6 1
 
< 0.1%
http://n2t.net/ark:/65665/30006cd83-36b3-4629-86db-f5a28307189f 1
 
< 0.1%
http://n2t.net/ark:/65665/30007a443-7a0a-49a9-9c54-cae1342160a6 1
 
< 0.1%
http://n2t.net/ark:/65665/300098b69-426b-451c-a675-27a1b7bb5b60 1
 
< 0.1%
http://n2t.net/ark:/65665/3000a9424-501b-43e7-a337-ee632a8fa9d0 1
 
< 0.1%
http://n2t.net/ark:/65665/3000ef5c5-8164-4ad4-b093-79821f58ace8 1
 
< 0.1%
http://n2t.net/ark:/65665/3000ff086-55d6-4f50-81a9-fc07e565e180 1
 
< 0.1%
http://n2t.net/ark:/65665/300114e18-4d31-4558-acc1-47ce8dd8940c 1
 
< 0.1%
Other values (338084) 338084
> 99.9%
2025-01-07T12:09:52.217563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1690470
 
7.9%
6 1647936
 
7.7%
- 1352376
 
6.3%
t 1352376
 
6.3%
5 1309841
 
6.1%
a 1056185
 
5.0%
4 972781
 
4.6%
3 972418
 
4.6%
2 971788
 
4.6%
e 970508
 
4.6%
Other values (16) 9003243
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21299922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 1690470
 
7.9%
6 1647936
 
7.7%
- 1352376
 
6.3%
t 1352376
 
6.3%
5 1309841
 
6.1%
a 1056185
 
5.0%
4 972781
 
4.6%
3 972418
 
4.6%
2 971788
 
4.6%
e 970508
 
4.6%
Other values (16) 9003243
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21299922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 1690470
 
7.9%
6 1647936
 
7.7%
- 1352376
 
6.3%
t 1352376
 
6.3%
5 1309841
 
6.1%
a 1056185
 
5.0%
4 972781
 
4.6%
3 972418
 
4.6%
2 971788
 
4.6%
e 970508
 
4.6%
Other values (16) 9003243
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21299922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 1690470
 
7.9%
6 1647936
 
7.7%
- 1352376
 
6.3%
t 1352376
 
6.3%
5 1309841
 
6.1%
a 1056185
 
5.0%
4 972781
 
4.6%
3 972418
 
4.6%
2 971788
 
4.6%
e 970508
 
4.6%
Other values (16) 9003243
42.3%

catalogNumber
Text

Missing 

Distinct225831
Distinct (%)84.4%
Missing70677
Missing (%)20.9%
Memory size2.6 MiB
2025-01-07T12:09:52.481238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length20
Mean length14.08573127
Min length9

Characters and Unicode

Total characters3766764
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192785 ?
Unique (%)72.1%

Sample

1st rowUSNMENT00976719.2
2nd rowUSNM 1566725
3rd rowUSNM 1430312
4th rowUSNM 1477111
5th rowUSNMENT01646520
ValueCountFrequency (%)
usnm 146196
33.4%
herp 7474
 
1.7%
tissue 7183
 
1.6%
us 2191
 
0.5%
lot 2187
 
0.5%
2187
 
0.5%
wet 2187
 
0.5%
image 291
 
0.1%
594492 64
 
< 0.1%
1487948 58
 
< 0.1%
Other values (223433) 267295
61.1%
2025-01-07T12:09:52.825440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 384256
 
10.2%
1 338698
 
9.0%
0 282088
 
7.5%
S 267418
 
7.1%
U 267417
 
7.1%
M 265226
 
7.0%
4 250702
 
6.7%
6 201321
 
5.3%
3 187391
 
5.0%
2 174893
 
4.6%
Other values (26) 1147354
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3766764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 384256
 
10.2%
1 338698
 
9.0%
0 282088
 
7.5%
S 267418
 
7.1%
U 267417
 
7.1%
M 265226
 
7.0%
4 250702
 
6.7%
6 201321
 
5.3%
3 187391
 
5.0%
2 174893
 
4.6%
Other values (26) 1147354
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3766764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 384256
 
10.2%
1 338698
 
9.0%
0 282088
 
7.5%
S 267418
 
7.1%
U 267417
 
7.1%
M 265226
 
7.0%
4 250702
 
6.7%
6 201321
 
5.3%
3 187391
 
5.0%
2 174893
 
4.6%
Other values (26) 1147354
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3766764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 384256
 
10.2%
1 338698
 
9.0%
0 282088
 
7.5%
S 267418
 
7.1%
U 267417
 
7.1%
M 265226
 
7.0%
4 250702
 
6.7%
6 201321
 
5.3%
3 187391
 
5.0%
2 174893
 
4.6%
Other values (26) 1147354
30.5%

recordNumber
Text

Missing 

Distinct102948
Distinct (%)65.8%
Missing181582
Missing (%)53.7%
Memory size2.6 MiB
2025-01-07T12:09:53.044590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length87
Median length53
Mean length8.259181405
Min length1

Characters and Unicode

Total characters1292661
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67344 ?
Unique (%)43.0%

Sample

1st rowT548-A9-TW19
2nd rowBMOO-09792
3rd rowJC3629
4th row707
5th rowmbio988
ValueCountFrequency (%)
blz 5367
 
2.9%
d&ml 4441
 
2.4%
1570
 
0.8%
tree 1340
 
0.7%
tag 1340
 
0.7%
flmoo 1323
 
0.7%
blb 1217
 
0.6%
sms 1215
 
0.6%
bah 989
 
0.5%
tob 834
 
0.4%
Other values (93496) 168604
89.6%
2025-01-07T12:09:53.339095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 122723
 
9.5%
2 92650
 
7.2%
0 89200
 
6.9%
3 72330
 
5.6%
- 60851
 
4.7%
5 57939
 
4.5%
4 57585
 
4.5%
6 53715
 
4.2%
8 52782
 
4.1%
7 52215
 
4.0%
Other values (66) 580671
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1292661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 122723
 
9.5%
2 92650
 
7.2%
0 89200
 
6.9%
3 72330
 
5.6%
- 60851
 
4.7%
5 57939
 
4.5%
4 57585
 
4.5%
6 53715
 
4.2%
8 52782
 
4.1%
7 52215
 
4.0%
Other values (66) 580671
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1292661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 122723
 
9.5%
2 92650
 
7.2%
0 89200
 
6.9%
3 72330
 
5.6%
- 60851
 
4.7%
5 57939
 
4.5%
4 57585
 
4.5%
6 53715
 
4.2%
8 52782
 
4.1%
7 52215
 
4.0%
Other values (66) 580671
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1292661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 122723
 
9.5%
2 92650
 
7.2%
0 89200
 
6.9%
3 72330
 
5.6%
- 60851
 
4.7%
5 57939
 
4.5%
4 57585
 
4.5%
6 53715
 
4.2%
8 52782
 
4.1%
7 52215
 
4.0%
Other values (66) 580671
44.9%

recordedBy
Text

Missing 

Distinct8090
Distinct (%)3.0%
Missing70120
Missing (%)20.7%
Memory size2.6 MiB
2025-01-07T12:09:53.539878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length161
Median length107
Mean length24.15374253
Min length1

Characters and Unicode

Total characters6472575
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique912 ?
Unique (%)0.3%

Sample

1st rowR. Wielgus
2nd rowR. Vrijenhoek
3rd rowS. McPherson
4th rowK. Crandall, H. Robinson, J. Buhay & A. Toon
5th rowTibet-MacArthur, D. A. Bell, V. A. Funk, S. Ge, Y. Meng, Z. Nie, R. Ree, J. Wen, J. Yue & W. Zuo
ValueCountFrequency (%)
115458
 
8.9%
m 70969
 
5.5%
j 68929
 
5.3%
r 47195
 
3.6%
d 44002
 
3.4%
c 43587
 
3.4%
s 40805
 
3.1%
k 35410
 
2.7%
l 29135
 
2.2%
a 28392
 
2.2%
Other values (5513) 775991
59.7%
2025-01-07T12:09:53.824700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1031899
15.9%
. 564920
 
8.7%
e 432034
 
6.7%
a 359856
 
5.6%
n 295498
 
4.6%
r 285565
 
4.4%
i 278605
 
4.3%
l 261098
 
4.0%
o 258914
 
4.0%
t 195845
 
3.0%
Other values (73) 2508341
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6472575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1031899
15.9%
. 564920
 
8.7%
e 432034
 
6.7%
a 359856
 
5.6%
n 295498
 
4.6%
r 285565
 
4.4%
i 278605
 
4.3%
l 261098
 
4.0%
o 258914
 
4.0%
t 195845
 
3.0%
Other values (73) 2508341
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6472575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1031899
15.9%
. 564920
 
8.7%
e 432034
 
6.7%
a 359856
 
5.6%
n 295498
 
4.6%
r 285565
 
4.4%
i 278605
 
4.3%
l 261098
 
4.0%
o 258914
 
4.0%
t 195845
 
3.0%
Other values (73) 2508341
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6472575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1031899
15.9%
. 564920
 
8.7%
e 432034
 
6.7%
a 359856
 
5.6%
n 295498
 
4.6%
r 285565
 
4.4%
i 278605
 
4.3%
l 261098
 
4.0%
o 258914
 
4.0%
t 195845
 
3.0%
Other values (73) 2508341
38.8%

recordedByID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

individualCount
Real number (ℝ)

Missing  Skewed 

Distinct19
Distinct (%)< 0.1%
Missing39347
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean1.00418749
Minimum0
Maximum36
Zeros2658
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:53.894707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2671031445
Coefficient of variation (CV)0.265989317
Kurtosis3075.338026
Mean1.00418749
Median Absolute Deviation (MAD)0
Skewness36.58205118
Sum299998
Variance0.0713440898
MonotonicityNot monotonic
2025-01-07T12:09:53.949715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 294711
87.2%
0 2658
 
0.8%
4 440
 
0.1%
2 363
 
0.1%
5 280
 
0.1%
3 226
 
0.1%
10 26
 
< 0.1%
6 20
 
< 0.1%
8 5
 
< 0.1%
7 4
 
< 0.1%
Other values (9) 14
 
< 0.1%
(Missing) 39347
 
11.6%
ValueCountFrequency (%)
0 2658
 
0.8%
1 294711
87.2%
2 363
 
0.1%
3 226
 
0.1%
4 440
 
0.1%
ValueCountFrequency (%)
36 1
< 0.1%
31 1
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
24 1
< 0.1%

organismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

organismQuantityType
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

sex
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing265741
Missing (%)78.6%
Memory size2.6 MiB
2025-01-07T12:09:53.991381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length4
Mean length4.888021229
Min length4

Characters and Unicode

Total characters353663
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowFEMALE
5th rowMALE
ValueCountFrequency (%)
male 40483
56.0%
female 31797
43.9%
hermaphrodite 73
 
0.1%
2025-01-07T12:09:54.098741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 104223
29.5%
M 72353
20.5%
A 72353
20.5%
L 72280
20.4%
F 31797
 
9.0%
H 146
 
< 0.1%
R 146
 
< 0.1%
P 73
 
< 0.1%
O 73
 
< 0.1%
D 73
 
< 0.1%
Other values (2) 146
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 353663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 104223
29.5%
M 72353
20.5%
A 72353
20.5%
L 72280
20.4%
F 31797
 
9.0%
H 146
 
< 0.1%
R 146
 
< 0.1%
P 73
 
< 0.1%
O 73
 
< 0.1%
D 73
 
< 0.1%
Other values (2) 146
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 353663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 104223
29.5%
M 72353
20.5%
A 72353
20.5%
L 72280
20.4%
F 31797
 
9.0%
H 146
 
< 0.1%
R 146
 
< 0.1%
P 73
 
< 0.1%
O 73
 
< 0.1%
D 73
 
< 0.1%
Other values (2) 146
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 353663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 104223
29.5%
M 72353
20.5%
A 72353
20.5%
L 72280
20.4%
F 31797
 
9.0%
H 146
 
< 0.1%
R 146
 
< 0.1%
P 73
 
< 0.1%
O 73
 
< 0.1%
D 73
 
< 0.1%
Other values (2) 146
 
< 0.1%

lifeStage
Text

Missing 

Distinct25
Distinct (%)< 0.1%
Missing209004
Missing (%)61.8%
Memory size2.6 MiB
2025-01-07T12:09:54.247371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.126787513
Min length3

Characters and Unicode

Total characters661817
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAdult
2nd rowAdult
3rd rowAdult
4th rowAdult
5th rowAdult
ValueCountFrequency (%)
adult 120982
93.7%
juvenile 3007
 
2.3%
larva 1688
 
1.3%
flowering 959
 
0.7%
unknown 541
 
0.4%
subadult 513
 
0.4%
eft 308
 
0.2%
immature 251
 
0.2%
veliger 163
 
0.1%
fruiting 134
 
0.1%
Other values (15) 544
 
0.4%
2025-01-07T12:09:54.366725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 125703
19.0%
u 125581
19.0%
t 122330
18.5%
d 121514
18.4%
A 120982
18.3%
e 7872
 
1.2%
n 5826
 
0.9%
v 4696
 
0.7%
a 4561
 
0.7%
i 4472
 
0.7%
Other values (28) 18280
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 661817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 125703
19.0%
u 125581
19.0%
t 122330
18.5%
d 121514
18.4%
A 120982
18.3%
e 7872
 
1.2%
n 5826
 
0.9%
v 4696
 
0.7%
a 4561
 
0.7%
i 4472
 
0.7%
Other values (28) 18280
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 661817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 125703
19.0%
u 125581
19.0%
t 122330
18.5%
d 121514
18.4%
A 120982
18.3%
e 7872
 
1.2%
n 5826
 
0.9%
v 4696
 
0.7%
a 4561
 
0.7%
i 4472
 
0.7%
Other values (28) 18280
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 661817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 125703
19.0%
u 125581
19.0%
t 122330
18.5%
d 121514
18.4%
A 120982
18.3%
e 7872
 
1.2%
n 5826
 
0.9%
v 4696
 
0.7%
a 4561
 
0.7%
i 4472
 
0.7%
Other values (28) 18280
 
2.8%

reproductiveCondition
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

caste
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

behavior
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

vitality
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

establishmentMeans
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

degreeOfEstablishment
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

pathway
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

georeferenceVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

occurrenceStatus
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:54.410864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2366658
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENT
2nd rowPRESENT
3rd rowPRESENT
4th rowPRESENT
5th rowPRESENT
ValueCountFrequency (%)
present 338094
100.0%
2025-01-07T12:09:54.507611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 676188
28.6%
P 338094
14.3%
R 338094
14.3%
S 338094
14.3%
N 338094
14.3%
T 338094
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2366658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 676188
28.6%
P 338094
14.3%
R 338094
14.3%
S 338094
14.3%
N 338094
14.3%
T 338094
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2366658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 676188
28.6%
P 338094
14.3%
R 338094
14.3%
S 338094
14.3%
N 338094
14.3%
T 338094
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2366658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 676188
28.6%
P 338094
14.3%
R 338094
14.3%
S 338094
14.3%
N 338094
14.3%
T 338094
14.3%

preparations
Text

Missing 

Distinct34
Distinct (%)< 0.1%
Missing251111
Missing (%)74.3%
Memory size2.6 MiB
2025-01-07T12:09:54.558819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length142
Median length6
Mean length6.19215249
Min length4

Characters and Unicode

Total characters538612
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowFrozen
2nd rowFrozen
3rd rowFrozen
4th rowFrozen
5th rowFrozen
ValueCountFrequency (%)
frozen 72559
79.3%
vial 6698
 
7.3%
ethanol 4918
 
5.4%
wet 2268
 
2.5%
lot 2268
 
2.5%
drained 1063
 
1.2%
photograph 626
 
0.7%
biorepository 456
 
0.5%
alcohol 197
 
0.2%
148
 
0.2%
Other values (11) 295
 
0.3%
2025-01-07T12:09:54.683206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 82904
15.4%
n 78637
14.6%
e 76402
14.2%
r 75218
14.0%
z 72559
13.5%
F 72209
13.4%
l 14333
 
2.7%
a 13317
 
2.5%
t 10591
 
2.0%
i 8773
 
1.6%
Other values (37) 33669
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 538612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 82904
15.4%
n 78637
14.6%
e 76402
14.2%
r 75218
14.0%
z 72559
13.5%
F 72209
13.4%
l 14333
 
2.7%
a 13317
 
2.5%
t 10591
 
2.0%
i 8773
 
1.6%
Other values (37) 33669
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 538612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 82904
15.4%
n 78637
14.6%
e 76402
14.2%
r 75218
14.0%
z 72559
13.5%
F 72209
13.4%
l 14333
 
2.7%
a 13317
 
2.5%
t 10591
 
2.0%
i 8773
 
1.6%
Other values (37) 33669
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 538612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 82904
15.4%
n 78637
14.6%
e 76402
14.2%
r 75218
14.0%
z 72559
13.5%
F 72209
13.4%
l 14333
 
2.7%
a 13317
 
2.5%
t 10591
 
2.0%
i 8773
 
1.6%
Other values (37) 33669
6.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:54.733110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.38328985
Min length2

Characters and Unicode

Total characters4186716
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowin collection
2nd rowin collection
3rd rowin collection
4th rowin collection
5th rowin collection
ValueCountFrequency (%)
in 298321
46.9%
collection 298321
46.9%
consumed 38009
 
6.0%
yes 943
 
0.1%
no 821
 
0.1%
2025-01-07T12:09:54.835538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 635472
15.2%
o 635472
15.2%
c 634651
15.2%
i 596642
14.3%
l 596642
14.3%
e 337273
8.1%
298321
7.1%
t 298321
7.1%
s 38952
 
0.9%
u 38009
 
0.9%
Other values (3) 76961
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4186716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 635472
15.2%
o 635472
15.2%
c 634651
15.2%
i 596642
14.3%
l 596642
14.3%
e 337273
8.1%
298321
7.1%
t 298321
7.1%
s 38952
 
0.9%
u 38009
 
0.9%
Other values (3) 76961
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4186716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 635472
15.2%
o 635472
15.2%
c 634651
15.2%
i 596642
14.3%
l 596642
14.3%
e 337273
8.1%
298321
7.1%
t 298321
7.1%
s 38952
 
0.9%
u 38009
 
0.9%
Other values (3) 76961
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4186716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 635472
15.2%
o 635472
15.2%
c 634651
15.2%
i 596642
14.3%
l 596642
14.3%
e 337273
8.1%
298321
7.1%
t 298321
7.1%
s 38952
 
0.9%
u 38009
 
0.9%
Other values (3) 76961
 
1.8%

associatedOccurrences
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

associatedReferences
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

associatedSequences
Text

Missing 

Distinct25139
Distinct (%)76.9%
Missing305424
Missing (%)90.3%
Memory size2.6 MiB
2025-01-07T12:09:55.054403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length206
Median length8
Mean length11.03008877
Min length8

Characters and Unicode

Total characters360353
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17608 ?
Unique (%)53.9%

Sample

1st rowMW204230;MW124559
2nd rowMW982336
3rd rowMF785606;MF785913
4th rowMN344605
5th rowJQ840329
ValueCountFrequency (%)
mw983371 2
 
< 0.1%
mw983528 2
 
< 0.1%
kt733269 2
 
< 0.1%
mw983103 2
 
< 0.1%
mw982441 2
 
< 0.1%
mw278063 2
 
< 0.1%
mw983009 2
 
< 0.1%
mw278258 2
 
< 0.1%
mg968184 2
 
< 0.1%
mn346122 2
 
< 0.1%
Other values (25129) 32650
99.9%
2025-01-07T12:09:55.355735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 34014
 
9.4%
3 32561
 
9.0%
4 30927
 
8.6%
9 27656
 
7.7%
M 27232
 
7.6%
2 27065
 
7.5%
7 23931
 
6.6%
0 23180
 
6.4%
5 21355
 
5.9%
1 21111
 
5.9%
Other values (24) 91321
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 34014
 
9.4%
3 32561
 
9.0%
4 30927
 
8.6%
9 27656
 
7.7%
M 27232
 
7.6%
2 27065
 
7.5%
7 23931
 
6.6%
0 23180
 
6.4%
5 21355
 
5.9%
1 21111
 
5.9%
Other values (24) 91321
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 34014
 
9.4%
3 32561
 
9.0%
4 30927
 
8.6%
9 27656
 
7.7%
M 27232
 
7.6%
2 27065
 
7.5%
7 23931
 
6.6%
0 23180
 
6.4%
5 21355
 
5.9%
1 21111
 
5.9%
Other values (24) 91321
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 34014
 
9.4%
3 32561
 
9.0%
4 30927
 
8.6%
9 27656
 
7.7%
M 27232
 
7.6%
2 27065
 
7.5%
7 23931
 
6.6%
0 23180
 
6.4%
5 21355
 
5.9%
1 21111
 
5.9%
Other values (24) 91321
25.3%

associatedTaxa
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

otherCatalogNumbers
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

occurrenceRemarks
Text

Missing 

Distinct28684
Distinct (%)19.8%
Missing193547
Missing (%)57.2%
Memory size2.6 MiB
2025-01-07T12:09:55.593286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length282633
Median length61
Mean length83.85489841
Min length1

Characters and Unicode

Total characters12120974
Distinct characters130
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19947 ?
Unique (%)13.8%

Sample

1st rowOne leg removed for genetic sampling while on loan to GUELPH.
2nd rowOrder: 10948; Box Number: MBARI_0136: Box Position: B/4
3rd rowOne leg removed for genetic sampling while on loan to GUELPH.
4th rowOriginally cataloged as an image record because field notes indicated there was a photovoucher for the specimen. When the images were cataloged in early 2020, no photos were found for this specimen so the record was changed to a Genetic Sample (DNA) with no voucher.
5th rowEntire tissue sample consumed for DNA extraction. Specimen voucher located at Museum National d'Histoire Naturelle, Paris.
ValueCountFrequency (%)
for 114846
 
5.9%
on 113429
 
5.8%
to 111972
 
5.7%
genetic 110770
 
5.7%
while 109786
 
5.6%
sampling 108913
 
5.6%
loan 108870
 
5.6%
removed 108857
 
5.6%
guelph 108797
 
5.6%
one 105620
 
5.4%
Other values (46309) 846419
43.4%
2025-01-07T12:09:55.880873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1744442
 
14.4%
e 1113548
 
9.2%
o 806325
 
6.7%
n 721471
 
6.0%
l 597748
 
4.9%
i 588547
 
4.9%
a 470216
 
3.9%
t 429603
 
3.5%
r 425976
 
3.5%
g 361714
 
3.0%
Other values (120) 4861384
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12120974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1744442
 
14.4%
e 1113548
 
9.2%
o 806325
 
6.7%
n 721471
 
6.0%
l 597748
 
4.9%
i 588547
 
4.9%
a 470216
 
3.9%
t 429603
 
3.5%
r 425976
 
3.5%
g 361714
 
3.0%
Other values (120) 4861384
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12120974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1744442
 
14.4%
e 1113548
 
9.2%
o 806325
 
6.7%
n 721471
 
6.0%
l 597748
 
4.9%
i 588547
 
4.9%
a 470216
 
3.9%
t 429603
 
3.5%
r 425976
 
3.5%
g 361714
 
3.0%
Other values (120) 4861384
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12120974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1744442
 
14.4%
e 1113548
 
9.2%
o 806325
 
6.7%
n 721471
 
6.0%
l 597748
 
4.9%
i 588547
 
4.9%
a 470216
 
3.9%
t 429603
 
3.5%
r 425976
 
3.5%
g 361714
 
3.0%
Other values (120) 4861384
40.1%

organismID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

organismName
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:09:55.930897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEML
ValueCountFrequency (%)
eml 1
100.0%
2025-01-07T12:09:56.023582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

organismScope
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:09:56.074701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters24
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2024-12-01T12:07:33.811Z
ValueCountFrequency (%)
2024-12-01t12:07:33.811z 1
100.0%
2025-01-07T12:09:56.176421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
20.8%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
3 2
 
8.3%
: 2
 
8.3%
4 1
 
4.2%
T 1
 
4.2%
7 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5
20.8%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
3 2
 
8.3%
: 2
 
8.3%
4 1
 
4.2%
T 1
 
4.2%
7 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5
20.8%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
3 2
 
8.3%
: 2
 
8.3%
4 1
 
4.2%
T 1
 
4.2%
7 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5
20.8%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
3 2
 
8.3%
: 2
 
8.3%
4 1
 
4.2%
T 1
 
4.2%
7 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

associatedOrganisms
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:09:56.226616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters24
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2024-12-01T11:07:21.711Z
ValueCountFrequency (%)
2024-12-01t11:07:21.711z 1
100.0%
2025-01-07T12:09:56.329808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7
29.2%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
7 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
. 1
 
4.2%
Z 1
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7
29.2%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
7 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
. 1
 
4.2%
Z 1
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7
29.2%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
7 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
. 1
 
4.2%
Z 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7
29.2%
2 4
16.7%
0 3
12.5%
- 2
 
8.3%
7 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
. 1
 
4.2%
Z 1
 
4.2%

previousIdentifications
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
True
 
1
(Missing)
338093 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 338093
> 99.9%
2025-01-07T12:09:56.386687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

organismRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

materialEntityID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

materialEntityRemarks
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
False
 
1
(Missing)
338093 
ValueCountFrequency (%)
False 1
 
< 0.1%
(Missing) 338093
> 99.9%
2025-01-07T12:09:56.420647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

verbatimLabel
Unsupported

Missing  Rejected  Unsupported 

Missing338089
Missing (%)> 99.9%
Memory size2.6 MiB

materialSampleID
Text

Missing 

Distinct253108
Distinct (%)100.0%
Missing84986
Missing (%)25.1%
Memory size2.6 MiB
2025-01-07T12:09:56.721102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length7
Mean length7.000031607
Min length7

Characters and Unicode

Total characters1771764
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253108 ?
Unique (%)100.0%

Sample

1st rowAR5TC43
2nd rowAL2IC84
3rd rowAF9HI08
4th rowAD5JZ99
5th rowAE0OQ35
ValueCountFrequency (%)
ah7zz15 1
 
< 0.1%
ar5tc43 1
 
< 0.1%
al2ic84 1
 
< 0.1%
af9hi08 1
 
< 0.1%
ac7bl99 1
 
< 0.1%
ao6qr52 1
 
< 0.1%
al5lp59 1
 
< 0.1%
ah6yy22 1
 
< 0.1%
am1rc62 1
 
< 0.1%
al0gj55 1
 
< 0.1%
Other values (253098) 253098
> 99.9%
2025-01-07T12:09:57.219873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 287625
 
16.2%
7 79818
 
4.5%
1 77867
 
4.4%
2 77272
 
4.4%
0 77025
 
4.3%
4 76603
 
4.3%
5 76332
 
4.3%
3 76014
 
4.3%
9 74989
 
4.2%
6 73630
 
4.2%
Other values (29) 794589
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1771764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 287625
 
16.2%
7 79818
 
4.5%
1 77867
 
4.4%
2 77272
 
4.4%
0 77025
 
4.3%
4 76603
 
4.3%
5 76332
 
4.3%
3 76014
 
4.3%
9 74989
 
4.2%
6 73630
 
4.2%
Other values (29) 794589
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1771764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 287625
 
16.2%
7 79818
 
4.5%
1 77867
 
4.4%
2 77272
 
4.4%
0 77025
 
4.3%
4 76603
 
4.3%
5 76332
 
4.3%
3 76014
 
4.3%
9 74989
 
4.2%
6 73630
 
4.2%
Other values (29) 794589
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1771764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 287625
 
16.2%
7 79818
 
4.5%
1 77867
 
4.4%
2 77272
 
4.4%
0 77025
 
4.3%
4 76603
 
4.3%
5 76332
 
4.3%
3 76014
 
4.3%
9 74989
 
4.2%
6 73630
 
4.2%
Other values (29) 794589
44.8%

eventID
Unsupported

Missing  Rejected  Unsupported 

Missing338092
Missing (%)> 99.9%
Memory size2.6 MiB

parentEventID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:09:57.284100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPanama
ValueCountFrequency (%)
panama 1
100.0%
2025-01-07T12:09:57.392111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
50.0%
P 1
 
16.7%
n 1
 
16.7%
m 1
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
50.0%
P 1
 
16.7%
n 1
 
16.7%
m 1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
50.0%
P 1
 
16.7%
n 1
 
16.7%
m 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
50.0%
P 1
 
16.7%
n 1
 
16.7%
m 1
 
16.7%

eventType
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:09:57.441751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPAN.5_1
ValueCountFrequency (%)
pan.5_1 1
100.0%
2025-01-07T12:09:57.546579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 1
14.3%
A 1
14.3%
N 1
14.3%
. 1
14.3%
5 1
14.3%
_ 1
14.3%
1 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1
14.3%
A 1
14.3%
N 1
14.3%
. 1
14.3%
5 1
14.3%
_ 1
14.3%
1 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1
14.3%
A 1
14.3%
N 1
14.3%
. 1
14.3%
5 1
14.3%
_ 1
14.3%
1 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1
14.3%
A 1
14.3%
N 1
14.3%
. 1
14.3%
5 1
14.3%
_ 1
14.3%
1 1
14.3%

fieldNumber
Text

Missing 

Distinct7065
Distinct (%)10.0%
Missing267153
Missing (%)79.0%
Memory size2.6 MiB
2025-01-07T12:09:57.751667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length43
Mean length11.55329076
Min length1

Characters and Unicode

Total characters819602
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2664 ?
Unique (%)3.8%

Sample

1st rowMBARI/T548
2nd rowMBIO/BIZ-231
3rd rowMoorea F-06-12
4th rowMBARI/T488
5th rowAL-4097
ValueCountFrequency (%)
cb 3399
 
3.7%
moorea 3150
 
3.5%
fp 1215
 
1.3%
lrp 1032
 
1.1%
bah 989
 
1.1%
tob 834
 
0.9%
cur 810
 
0.9%
mbio/080611_minv_014 626
 
0.7%
dgs 506
 
0.6%
sec18-07 504
 
0.6%
Other values (7236) 78011
85.7%
2025-01-07T12:09:58.062734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 78614
 
9.6%
- 70093
 
8.6%
1 62300
 
7.6%
B 45896
 
5.6%
2 43979
 
5.4%
I 35433
 
4.3%
M 34390
 
4.2%
A 34133
 
4.2%
3 27058
 
3.3%
8 21657
 
2.6%
Other values (63) 366049
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 819602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 78614
 
9.6%
- 70093
 
8.6%
1 62300
 
7.6%
B 45896
 
5.6%
2 43979
 
5.4%
I 35433
 
4.3%
M 34390
 
4.2%
A 34133
 
4.2%
3 27058
 
3.3%
8 21657
 
2.6%
Other values (63) 366049
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 819602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 78614
 
9.6%
- 70093
 
8.6%
1 62300
 
7.6%
B 45896
 
5.6%
2 43979
 
5.4%
I 35433
 
4.3%
M 34390
 
4.2%
A 34133
 
4.2%
3 27058
 
3.3%
8 21657
 
2.6%
Other values (63) 366049
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 819602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 78614
 
9.6%
- 70093
 
8.6%
1 62300
 
7.6%
B 45896
 
5.6%
2 43979
 
5.4%
I 35433
 
4.3%
M 34390
 
4.2%
A 34133
 
4.2%
3 27058
 
3.3%
8 21657
 
2.6%
Other values (63) 366049
44.7%

eventDate
Text

Missing 

Distinct23011
Distinct (%)7.2%
Missing16903
Missing (%)5.0%
Memory size2.6 MiB
2025-01-07T12:09:58.275431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length10
Mean length11.0585446
Min length4

Characters and Unicode

Total characters3551905
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1361 ?
Unique (%)0.4%

Sample

1st row1977-05-21
2nd row2003-04-05
3rd row2009-12-05
4th row2006-09-14
5th row2003-05-01/2003-05-13
ValueCountFrequency (%)
2018-03-19/2018-03-23 1119
 
0.3%
2016-02-22/2016-03-09 840
 
0.3%
2008-06-11 649
 
0.2%
2017-05-26 623
 
0.2%
2015-05-09 524
 
0.2%
2017-05-23 518
 
0.2%
2017-05-30 515
 
0.2%
2006-03-12 513
 
0.2%
2017-08-14 508
 
0.2%
2017-05-27 505
 
0.2%
Other values (23001) 314877
98.0%
2025-01-07T12:09:58.538110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 767941
21.6%
- 702212
19.8%
1 575030
16.2%
2 411303
11.6%
9 302111
 
8.5%
8 151019
 
4.3%
7 140049
 
3.9%
6 130946
 
3.7%
5 122995
 
3.5%
3 119921
 
3.4%
Other values (7) 128378
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3551905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 767941
21.6%
- 702212
19.8%
1 575030
16.2%
2 411303
11.6%
9 302111
 
8.5%
8 151019
 
4.3%
7 140049
 
3.9%
6 130946
 
3.7%
5 122995
 
3.5%
3 119921
 
3.4%
Other values (7) 128378
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3551905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 767941
21.6%
- 702212
19.8%
1 575030
16.2%
2 411303
11.6%
9 302111
 
8.5%
8 151019
 
4.3%
7 140049
 
3.9%
6 130946
 
3.7%
5 122995
 
3.5%
3 119921
 
3.4%
Other values (7) 128378
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3551905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 767941
21.6%
- 702212
19.8%
1 575030
16.2%
2 411303
11.6%
9 302111
 
8.5%
8 151019
 
4.3%
7 140049
 
3.9%
6 130946
 
3.7%
5 122995
 
3.5%
3 119921
 
3.4%
Other values (7) 128378
 
3.6%

eventTime
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing338093
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:09:58.599380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPinogana
ValueCountFrequency (%)
pinogana 1
100.0%
2025-01-07T12:09:58.820639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
25.0%
n 2
25.0%
i 1
12.5%
P 1
12.5%
o 1
12.5%
g 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
25.0%
n 2
25.0%
i 1
12.5%
P 1
12.5%
o 1
12.5%
g 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
25.0%
n 2
25.0%
i 1
12.5%
P 1
12.5%
o 1
12.5%
g 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
25.0%
n 2
25.0%
i 1
12.5%
P 1
12.5%
o 1
12.5%
g 1
12.5%

startDayOfYear
Unsupported

Missing  Rejected  Unsupported 

Missing19910
Missing (%)5.9%
Memory size2.6 MiB

endDayOfYear
Unsupported

Missing  Rejected  Unsupported 

Missing19910
Missing (%)5.9%
Memory size2.6 MiB

year
Unsupported

Missing  Rejected  Unsupported 

Missing17140
Missing (%)5.1%
Memory size2.6 MiB

month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)< 0.1%
Missing22792
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean6.395811
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:09:58.886172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.942523803
Coefficient of variation (CV)0.4600704747
Kurtosis-0.8370802027
Mean6.395811
Median Absolute Deviation (MAD)2
Skewness0.07506901235
Sum2016612
Variance8.658446333
MonotonicityNot monotonic
2025-01-07T12:09:58.938519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 42126
12.5%
6 36817
10.9%
7 36682
10.8%
8 30685
9.1%
4 28521
8.4%
3 27357
8.1%
9 25336
7.5%
10 23088
6.8%
11 20226
6.0%
2 15793
 
4.7%
Other values (2) 28671
8.5%
(Missing) 22792
6.7%
ValueCountFrequency (%)
1 15487
 
4.6%
2 15793
 
4.7%
3 27357
8.1%
4 28521
8.4%
5 42126
12.5%
ValueCountFrequency (%)
12 13184
3.9%
11 20226
6.0%
10 23088
6.8%
9 25336
7.5%
8 30685
9.1%

day
Unsupported

Missing  Rejected  Unsupported 

Missing52010
Missing (%)15.4%
Memory size2.6 MiB

verbatimEventDate
Text

Missing 

Distinct10231
Distinct (%)10.0%
Missing235843
Missing (%)69.8%
Memory size2.6 MiB
2025-01-07T12:09:59.138587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length72
Median length71
Mean length13.69981712
Min length1

Characters and Unicode

Total characters1400820
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2669 ?
Unique (%)2.6%

Sample

1st row4/5/2003 3:59:00 PM
2nd row2007 or prior, based on filename of source data sheet
3rd row14 Sep 2006
4th row10/11/2002 1:30:00 PM
5th row11 May 2014
ValueCountFrequency (%)
may 10951
 
3.6%
apr 6716
 
2.2%
pm 6650
 
2.2%
aug 5881
 
1.9%
5371
 
1.8%
2007 5227
 
1.7%
sep 5183
 
1.7%
mar 4904
 
1.6%
2008 4654
 
1.5%
june 4026
 
1.3%
Other values (3776) 242866
80.3%
2025-01-07T12:09:59.461730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200178
 
14.3%
0 158992
 
11.3%
1 143302
 
10.2%
2 117701
 
8.4%
9 73185
 
5.2%
e 40939
 
2.9%
8 37291
 
2.7%
a 35798
 
2.6%
3 32860
 
2.3%
r 32280
 
2.3%
Other values (66) 528294
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1400820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
200178
 
14.3%
0 158992
 
11.3%
1 143302
 
10.2%
2 117701
 
8.4%
9 73185
 
5.2%
e 40939
 
2.9%
8 37291
 
2.7%
a 35798
 
2.6%
3 32860
 
2.3%
r 32280
 
2.3%
Other values (66) 528294
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1400820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
200178
 
14.3%
0 158992
 
11.3%
1 143302
 
10.2%
2 117701
 
8.4%
9 73185
 
5.2%
e 40939
 
2.9%
8 37291
 
2.7%
a 35798
 
2.6%
3 32860
 
2.3%
r 32280
 
2.3%
Other values (66) 528294
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1400820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
200178
 
14.3%
0 158992
 
11.3%
1 143302
 
10.2%
2 117701
 
8.4%
9 73185
 
5.2%
e 40939
 
2.9%
8 37291
 
2.7%
a 35798
 
2.6%
3 32860
 
2.3%
r 32280
 
2.3%
Other values (66) 528294
37.7%

habitat
Text

Missing 

Distinct5074
Distinct (%)14.1%
Missing302025
Missing (%)89.3%
Memory size2.6 MiB
2025-01-07T12:09:59.688428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length382
Median length180
Mean length39.97022374
Min length1

Characters and Unicode

Total characters1441686
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1915 ?
Unique (%)5.3%

Sample

1st rowRocky slope with scattered shrubs. Moist soil on slope
2nd rowScrubland
3rd rowEcological remarks by collector(s): yes
4th rowCultivated/garden
5th rowbrushed from under rubble
ValueCountFrequency (%)
forest 9232
 
4.6%
and 8075
 
4.0%
with 6431
 
3.2%
by 4851
 
2.4%
remarks 4348
 
2.2%
ecological 4348
 
2.2%
collector(s 4343
 
2.2%
in 4299
 
2.1%
yes 3549
 
1.8%
slopes 2419
 
1.2%
Other values (4257) 150032
74.3%
2025-01-07T12:09:59.997834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
165858
 
11.5%
e 122882
 
8.5%
a 115017
 
8.0%
r 97779
 
6.8%
o 97024
 
6.7%
s 87680
 
6.1%
i 77128
 
5.3%
n 73943
 
5.1%
t 69140
 
4.8%
l 65307
 
4.5%
Other values (77) 469928
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1441686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
165858
 
11.5%
e 122882
 
8.5%
a 115017
 
8.0%
r 97779
 
6.8%
o 97024
 
6.7%
s 87680
 
6.1%
i 77128
 
5.3%
n 73943
 
5.1%
t 69140
 
4.8%
l 65307
 
4.5%
Other values (77) 469928
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1441686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
165858
 
11.5%
e 122882
 
8.5%
a 115017
 
8.0%
r 97779
 
6.8%
o 97024
 
6.7%
s 87680
 
6.1%
i 77128
 
5.3%
n 73943
 
5.1%
t 69140
 
4.8%
l 65307
 
4.5%
Other values (77) 469928
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1441686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
165858
 
11.5%
e 122882
 
8.5%
a 115017
 
8.0%
r 97779
 
6.8%
o 97024
 
6.7%
s 87680
 
6.1%
i 77128
 
5.3%
n 73943
 
5.1%
t 69140
 
4.8%
l 65307
 
4.5%
Other values (77) 469928
32.6%

samplingProtocol
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

sampleSizeValue
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

sampleSizeUnit
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

samplingEffort
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

fieldNotes
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

eventRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

locationID
Text

Missing 

Distinct4570
Distinct (%)8.5%
Missing284620
Missing (%)84.2%
Memory size2.6 MiB
2025-01-07T12:10:00.229923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length6.812862326
Min length1

Characters and Unicode

Total characters364311
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1199 ?
Unique (%)2.2%

Sample

1st rowT548
2nd rowBIZ-231
3rd rowT488
4th row02-10
5th rowVES117
ValueCountFrequency (%)
080611_minv_014 627
 
1.1%
site 469
 
0.8%
i 456
 
0.8%
trawl 456
 
0.8%
serc 326
 
0.6%
14 313
 
0.6%
v1951 308
 
0.5%
080608_minv_012 289
 
0.5%
21 275
 
0.5%
10 275
 
0.5%
Other values (4452) 53036
93.3%
2025-01-07T12:10:00.515449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37140
 
10.2%
1 34688
 
9.5%
- 19187
 
5.3%
2 18285
 
5.0%
I 15952
 
4.4%
_ 15373
 
4.2%
5 13776
 
3.8%
4 13693
 
3.8%
8 13263
 
3.6%
6 12669
 
3.5%
Other values (73) 170285
46.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37140
 
10.2%
1 34688
 
9.5%
- 19187
 
5.3%
2 18285
 
5.0%
I 15952
 
4.4%
_ 15373
 
4.2%
5 13776
 
3.8%
4 13693
 
3.8%
8 13263
 
3.6%
6 12669
 
3.5%
Other values (73) 170285
46.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37140
 
10.2%
1 34688
 
9.5%
- 19187
 
5.3%
2 18285
 
5.0%
I 15952
 
4.4%
_ 15373
 
4.2%
5 13776
 
3.8%
4 13693
 
3.8%
8 13263
 
3.6%
6 12669
 
3.5%
Other values (73) 170285
46.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37140
 
10.2%
1 34688
 
9.5%
- 19187
 
5.3%
2 18285
 
5.0%
I 15952
 
4.4%
_ 15373
 
4.2%
5 13776
 
3.8%
4 13693
 
3.8%
8 13263
 
3.6%
6 12669
 
3.5%
Other values (73) 170285
46.7%

higherGeographyID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

higherGeography
Text

Missing 

Distinct7779
Distinct (%)2.3%
Missing4531
Missing (%)1.3%
Memory size2.6 MiB
2025-01-07T12:10:00.733079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length128
Median length103
Mean length44.48305717
Min length4

Characters and Unicode

Total characters14837902
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique787 ?
Unique (%)0.2%

Sample

1st rowUnited States, Arizona, Cochise
2nd rowNorth Pacific Ocean, Gulf of California, Mexico
3rd rowSouth Pacific Ocean, French Polynesia, Society Islands, Moorea
4th rowUnited States, Arkansas
5th rowAsia-Temperate, China, Xizang, Nielamu (Nyalam) Xian
ValueCountFrequency (%)
states 150734
 
7.6%
united 150654
 
7.6%
north 101817
 
5.1%
ocean 69413
 
3.5%
pacific 66261
 
3.4%
america 65435
 
3.3%
stated 60307
 
3.0%
not 60307
 
3.0%
islands 44071
 
2.2%
atlantic 41374
 
2.1%
Other values (4525) 1167157
59.0%
2025-01-07T12:10:01.013659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1643967
 
11.1%
a 1472182
 
9.9%
t 1108058
 
7.5%
e 1084394
 
7.3%
i 1040753
 
7.0%
n 860928
 
5.8%
, 825692
 
5.6%
o 731207
 
4.9%
r 620008
 
4.2%
s 541802
 
3.7%
Other values (88) 4908911
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14837902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1643967
 
11.1%
a 1472182
 
9.9%
t 1108058
 
7.5%
e 1084394
 
7.3%
i 1040753
 
7.0%
n 860928
 
5.8%
, 825692
 
5.6%
o 731207
 
4.9%
r 620008
 
4.2%
s 541802
 
3.7%
Other values (88) 4908911
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14837902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1643967
 
11.1%
a 1472182
 
9.9%
t 1108058
 
7.5%
e 1084394
 
7.3%
i 1040753
 
7.0%
n 860928
 
5.8%
, 825692
 
5.6%
o 731207
 
4.9%
r 620008
 
4.2%
s 541802
 
3.7%
Other values (88) 4908911
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14837902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1643967
 
11.1%
a 1472182
 
9.9%
t 1108058
 
7.5%
e 1084394
 
7.3%
i 1040753
 
7.0%
n 860928
 
5.8%
, 825692
 
5.6%
o 731207
 
4.9%
r 620008
 
4.2%
s 541802
 
3.7%
Other values (88) 4908911
33.1%

continent
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing57738
Missing (%)17.1%
Memory size2.6 MiB
2025-01-07T12:10:01.076989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length10.55335716
Min length4

Characters and Unicode

Total characters2958697
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowOCEANIA
3rd rowASIA
4th rowAFRICA
5th rowOCEANIA
ValueCountFrequency (%)
north_america 154617
55.2%
oceania 41626
 
14.8%
asia 32094
 
11.4%
south_america 30956
 
11.0%
africa 17455
 
6.2%
europe 3580
 
1.3%
antarctica 28
 
< 0.1%
2025-01-07T12:10:01.187259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 553580
18.7%
R 361253
12.2%
I 276776
9.4%
C 244710
8.3%
E 234359
7.9%
O 230779
7.8%
N 196271
 
6.6%
T 185629
 
6.3%
H 185573
 
6.3%
_ 185573
 
6.3%
Other values (5) 304194
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2958697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 553580
18.7%
R 361253
12.2%
I 276776
9.4%
C 244710
8.3%
E 234359
7.9%
O 230779
7.8%
N 196271
 
6.6%
T 185629
 
6.3%
H 185573
 
6.3%
_ 185573
 
6.3%
Other values (5) 304194
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2958697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 553580
18.7%
R 361253
12.2%
I 276776
9.4%
C 244710
8.3%
E 234359
7.9%
O 230779
7.8%
N 196271
 
6.6%
T 185629
 
6.3%
H 185573
 
6.3%
_ 185573
 
6.3%
Other values (5) 304194
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2958697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 553580
18.7%
R 361253
12.2%
I 276776
9.4%
C 244710
8.3%
E 234359
7.9%
O 230779
7.8%
N 196271
 
6.6%
T 185629
 
6.3%
H 185573
 
6.3%
_ 185573
 
6.3%
Other values (5) 304194
10.3%

waterBody
Text

Missing 

Distinct217
Distinct (%)0.2%
Missing231346
Missing (%)68.4%
Memory size2.6 MiB
2025-01-07T12:10:01.348774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length69
Median length53
Mean length20.41937085
Min length6

Characters and Unicode

Total characters2179727
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowNorth Pacific Ocean, Gulf of California
2nd rowSouth Pacific Ocean
3rd rowNorth Atlantic Ocean
4th rowPacific
5th rowNorth Pacific Ocean
ValueCountFrequency (%)
ocean 69162
21.1%
pacific 61578
18.8%
north 47089
14.4%
atlantic 41318
12.6%
south 18400
 
5.6%
sea 18234
 
5.6%
caribbean 14724
 
4.5%
bay 12118
 
3.7%
gulf 7267
 
2.2%
of 6749
 
2.1%
Other values (198) 31204
9.5%
2025-01-07T12:10:01.595595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 263495
12.1%
c 237624
10.9%
221095
 
10.1%
i 195151
 
9.0%
t 152988
 
7.0%
n 144138
 
6.6%
e 133315
 
6.1%
o 87930
 
4.0%
f 78762
 
3.6%
h 75874
 
3.5%
Other values (45) 589355
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2179727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 263495
12.1%
c 237624
10.9%
221095
 
10.1%
i 195151
 
9.0%
t 152988
 
7.0%
n 144138
 
6.6%
e 133315
 
6.1%
o 87930
 
4.0%
f 78762
 
3.6%
h 75874
 
3.5%
Other values (45) 589355
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2179727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 263495
12.1%
c 237624
10.9%
221095
 
10.1%
i 195151
 
9.0%
t 152988
 
7.0%
n 144138
 
6.6%
e 133315
 
6.1%
o 87930
 
4.0%
f 78762
 
3.6%
h 75874
 
3.5%
Other values (45) 589355
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2179727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 263495
12.1%
c 237624
10.9%
221095
 
10.1%
i 195151
 
9.0%
t 152988
 
7.0%
n 144138
 
6.6%
e 133315
 
6.1%
o 87930
 
4.0%
f 78762
 
3.6%
h 75874
 
3.5%
Other values (45) 589355
27.0%

islandGroup
Text

Missing 

Distinct100
Distinct (%)0.4%
Missing315374
Missing (%)93.3%
Memory size2.6 MiB
2025-01-07T12:10:01.718349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length21
Mean length14.50514965
Min length5

Characters and Unicode

Total characters329557
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowSociety Islands
2nd rowLeeward Antilles
3rd rowBahama Islands
4th rowSociety Islands
5th rowVisayas
ValueCountFrequency (%)
islands 15107
31.9%
society 10375
21.9%
leeward 3580
 
7.6%
antilles 3191
 
6.7%
îles 1360
 
2.9%
vent 1360
 
2.9%
du 1300
 
2.7%
cays 1105
 
2.3%
bahama 989
 
2.1%
group 827
 
1.7%
Other values (103) 8209
17.3%
2025-01-07T12:10:01.916085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 39205
11.9%
a 28802
 
8.7%
e 28031
 
8.5%
24683
 
7.5%
l 24467
 
7.4%
n 22700
 
6.9%
d 21934
 
6.7%
i 17625
 
5.3%
t 16043
 
4.9%
I 15495
 
4.7%
Other values (41) 90572
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 329557
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 39205
11.9%
a 28802
 
8.7%
e 28031
 
8.5%
24683
 
7.5%
l 24467
 
7.4%
n 22700
 
6.9%
d 21934
 
6.7%
i 17625
 
5.3%
t 16043
 
4.9%
I 15495
 
4.7%
Other values (41) 90572
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 329557
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 39205
11.9%
a 28802
 
8.7%
e 28031
 
8.5%
24683
 
7.5%
l 24467
 
7.4%
n 22700
 
6.9%
d 21934
 
6.7%
i 17625
 
5.3%
t 16043
 
4.9%
I 15495
 
4.7%
Other values (41) 90572
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 329557
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 39205
11.9%
a 28802
 
8.7%
e 28031
 
8.5%
24683
 
7.5%
l 24467
 
7.4%
n 22700
 
6.9%
d 21934
 
6.7%
i 17625
 
5.3%
t 16043
 
4.9%
I 15495
 
4.7%
Other values (41) 90572
27.5%

island
Text

Missing 

Distinct566
Distinct (%)1.0%
Missing279260
Missing (%)82.6%
Memory size2.6 MiB
2025-01-07T12:10:02.110320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length25
Mean length8.431383214
Min length3

Characters and Unicode

Total characters496052
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.1%

Sample

1st rowMoorea
2nd rowMoorea
3rd rowMindanao
4th rowKlein Curacao
5th rowMoorea
ValueCountFrequency (%)
moorea 15941
18.5%
cay 7341
 
8.5%
carrie 4785
 
5.5%
bow 4785
 
5.5%
island 4062
 
4.7%
curacao 3674
 
4.3%
oahu 2249
 
2.6%
luzon 2088
 
2.4%
borneo 2043
 
2.4%
atoll 914
 
1.1%
Other values (560) 38461
44.5%
2025-01-07T12:10:02.374616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 78531
15.8%
o 63637
12.8%
r 44894
 
9.1%
e 38281
 
7.7%
27509
 
5.5%
u 21061
 
4.2%
n 20954
 
4.2%
i 20832
 
4.2%
C 19923
 
4.0%
M 19688
 
4.0%
Other values (52) 140742
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 496052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 78531
15.8%
o 63637
12.8%
r 44894
 
9.1%
e 38281
 
7.7%
27509
 
5.5%
u 21061
 
4.2%
n 20954
 
4.2%
i 20832
 
4.2%
C 19923
 
4.0%
M 19688
 
4.0%
Other values (52) 140742
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 496052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 78531
15.8%
o 63637
12.8%
r 44894
 
9.1%
e 38281
 
7.7%
27509
 
5.5%
u 21061
 
4.2%
n 20954
 
4.2%
i 20832
 
4.2%
C 19923
 
4.0%
M 19688
 
4.0%
Other values (52) 140742
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 496052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 78531
15.8%
o 63637
12.8%
r 44894
 
9.1%
e 38281
 
7.7%
27509
 
5.5%
u 21061
 
4.2%
n 20954
 
4.2%
i 20832
 
4.2%
C 19923
 
4.0%
M 19688
 
4.0%
Other values (52) 140742
28.4%

countryCode
Text

Missing 

Distinct203
Distinct (%)0.1%
Missing11127
Missing (%)3.3%
Memory size2.6 MiB
2025-01-07T12:10:02.556384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters653934
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowMX
3rd rowPF
4th rowUS
5th rowCN
ValueCountFrequency (%)
us 150956
46.2%
pf 22993
 
7.0%
mx 10948
 
3.3%
pa 9204
 
2.8%
bz 9189
 
2.8%
mm 8052
 
2.5%
ph 6777
 
2.1%
gy 5990
 
1.8%
pg 4467
 
1.4%
cw 4291
 
1.3%
Other values (193) 94100
28.8%
2025-01-07T12:10:02.787536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 158858
24.3%
U 156371
23.9%
P 51767
 
7.9%
M 36656
 
5.6%
F 27494
 
4.2%
C 25318
 
3.9%
A 21589
 
3.3%
G 20634
 
3.2%
B 18213
 
2.8%
Z 16360
 
2.5%
Other values (16) 120674
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 653934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 158858
24.3%
U 156371
23.9%
P 51767
 
7.9%
M 36656
 
5.6%
F 27494
 
4.2%
C 25318
 
3.9%
A 21589
 
3.3%
G 20634
 
3.2%
B 18213
 
2.8%
Z 16360
 
2.5%
Other values (16) 120674
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 653934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 158858
24.3%
U 156371
23.9%
P 51767
 
7.9%
M 36656
 
5.6%
F 27494
 
4.2%
C 25318
 
3.9%
A 21589
 
3.3%
G 20634
 
3.2%
B 18213
 
2.8%
Z 16360
 
2.5%
Other values (16) 120674
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 653934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 158858
24.3%
U 156371
23.9%
P 51767
 
7.9%
M 36656
 
5.6%
F 27494
 
4.2%
C 25318
 
3.9%
A 21589
 
3.3%
G 20634
 
3.2%
B 18213
 
2.8%
Z 16360
 
2.5%
Other values (16) 120674
18.5%

stateProvince
Text

Missing 

Distinct1646
Distinct (%)0.6%
Missing66137
Missing (%)19.6%
Memory size2.6 MiB
2025-01-07T12:10:02.987273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length52
Median length42
Mean length9.616295959
Min length3

Characters and Unicode

Total characters2615219
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)< 0.1%

Sample

1st rowArizona
2nd rowArkansas
3rd rowXizang
4th rowLaikipia
5th rowFlorida
ValueCountFrequency (%)
california 17057
 
4.6%
florida 16471
 
4.4%
texas 14319
 
3.9%
virginia 13034
 
3.5%
stated 10630
 
2.9%
not 10630
 
2.9%
arizona 9677
 
2.6%
carolina 8845
 
2.4%
region 8363
 
2.3%
new 8067
 
2.2%
Other values (1667) 253487
68.4%
2025-01-07T12:10:03.268581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 361404
13.8%
i 256979
 
9.8%
n 192389
 
7.4%
o 190990
 
7.3%
r 175132
 
6.7%
e 143513
 
5.5%
s 116807
 
4.5%
t 109006
 
4.2%
l 104505
 
4.0%
98623
 
3.8%
Other values (72) 865871
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2615219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 361404
13.8%
i 256979
 
9.8%
n 192389
 
7.4%
o 190990
 
7.3%
r 175132
 
6.7%
e 143513
 
5.5%
s 116807
 
4.5%
t 109006
 
4.2%
l 104505
 
4.0%
98623
 
3.8%
Other values (72) 865871
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2615219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 361404
13.8%
i 256979
 
9.8%
n 192389
 
7.4%
o 190990
 
7.3%
r 175132
 
6.7%
e 143513
 
5.5%
s 116807
 
4.5%
t 109006
 
4.2%
l 104505
 
4.0%
98623
 
3.8%
Other values (72) 865871
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2615219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 361404
13.8%
i 256979
 
9.8%
n 192389
 
7.4%
o 190990
 
7.3%
r 175132
 
6.7%
e 143513
 
5.5%
s 116807
 
4.5%
t 109006
 
4.2%
l 104505
 
4.0%
98623
 
3.8%
Other values (72) 865871
33.1%

county
Text

Missing 

Distinct3053
Distinct (%)1.5%
Missing140475
Missing (%)41.5%
Memory size2.6 MiB
2025-01-07T12:10:03.469468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length35
Mean length10.83467683
Min length1

Characters and Unicode

Total characters2141138
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)0.1%

Sample

1st rowCochise
2nd rowNielamu (Nyalam) Xian
3rd row[Not Stated]
4th row[Not Stated]
5th row[Not Stated]
ValueCountFrequency (%)
not 49620
 
15.0%
stated 49620
 
15.0%
county 38478
 
11.6%
honolulu 5034
 
1.5%
san 4615
 
1.4%
st 3587
 
1.1%
cochise 3337
 
1.0%
lucie 3224
 
1.0%
island 2682
 
0.8%
xian 2350
 
0.7%
Other values (2542) 168921
51.0%
2025-01-07T12:10:03.834678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 236676
 
11.1%
o 187861
 
8.8%
a 183049
 
8.5%
e 150027
 
7.0%
n 138728
 
6.5%
133849
 
6.3%
u 85231
 
4.0%
i 84530
 
3.9%
d 76606
 
3.6%
r 76271
 
3.6%
Other values (73) 788310
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2141138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 236676
 
11.1%
o 187861
 
8.8%
a 183049
 
8.5%
e 150027
 
7.0%
n 138728
 
6.5%
133849
 
6.3%
u 85231
 
4.0%
i 84530
 
3.9%
d 76606
 
3.6%
r 76271
 
3.6%
Other values (73) 788310
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2141138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 236676
 
11.1%
o 187861
 
8.8%
a 183049
 
8.5%
e 150027
 
7.0%
n 138728
 
6.5%
133849
 
6.3%
u 85231
 
4.0%
i 84530
 
3.9%
d 76606
 
3.6%
r 76271
 
3.6%
Other values (73) 788310
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2141138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 236676
 
11.1%
o 187861
 
8.8%
a 183049
 
8.5%
e 150027
 
7.0%
n 138728
 
6.5%
133849
 
6.3%
u 85231
 
4.0%
i 84530
 
3.9%
d 76606
 
3.6%
r 76271
 
3.6%
Other values (73) 788310
36.8%

municipality
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

locality
Text

Missing 

Distinct31944
Distinct (%)10.5%
Missing34045
Missing (%)10.1%
Memory size2.6 MiB
2025-01-07T12:10:04.045610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length312
Median length249
Mean length40.81951593
Min length3

Characters and Unicode

Total characters12411133
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4485 ?
Unique (%)1.5%

Sample

1st rowCarr Canyon, Huachuca Mountains
2nd rowSociety Islands, Moorea, In front of Hilton
3rd rowAshdown
4th rowNielamu Zhen. Route 318 between Zhangmu and Nielamu (Nyalam) ca. 8 km from Zhangmu.
5th rowMpala Research Centre
ValueCountFrequency (%)
of 95416
 
4.7%
km 27888
 
1.4%
road 25983
 
1.3%
on 20774
 
1.0%
island 19621
 
1.0%
and 19459
 
1.0%
national 18145
 
0.9%
river 17516
 
0.9%
creek 15244
 
0.8%
at 14855
 
0.7%
Other values (27242) 1755591
86.5%
2025-01-07T12:10:04.362547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1726443
 
13.9%
a 1102256
 
8.9%
e 888496
 
7.2%
o 818946
 
6.6%
n 661740
 
5.3%
i 647279
 
5.2%
r 607431
 
4.9%
t 591988
 
4.8%
l 448950
 
3.6%
s 433370
 
3.5%
Other values (123) 4484234
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12411133
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1726443
 
13.9%
a 1102256
 
8.9%
e 888496
 
7.2%
o 818946
 
6.6%
n 661740
 
5.3%
i 647279
 
5.2%
r 607431
 
4.9%
t 591988
 
4.8%
l 448950
 
3.6%
s 433370
 
3.5%
Other values (123) 4484234
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12411133
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1726443
 
13.9%
a 1102256
 
8.9%
e 888496
 
7.2%
o 818946
 
6.6%
n 661740
 
5.3%
i 647279
 
5.2%
r 607431
 
4.9%
t 591988
 
4.8%
l 448950
 
3.6%
s 433370
 
3.5%
Other values (123) 4484234
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12411133
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1726443
 
13.9%
a 1102256
 
8.9%
e 888496
 
7.2%
o 818946
 
6.6%
n 661740
 
5.3%
i 647279
 
5.2%
r 607431
 
4.9%
t 591988
 
4.8%
l 448950
 
3.6%
s 433370
 
3.5%
Other values (123) 4484234
36.1%

verbatimLocality
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

verbatimElevation
Text

Missing 

Distinct913
Distinct (%)5.7%
Missing322170
Missing (%)95.3%
Memory size2.6 MiB
2025-01-07T12:10:04.595312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length27
Mean length6.528761618
Min length1

Characters and Unicode

Total characters103964
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)1.0%

Sample

1st row760 m
2nd row1050 ft
3rd row611 m
4th row73 m
5th row500 ft
ValueCountFrequency (%)
m 8065
23.4%
ft 7360
21.4%
ca 904
 
2.6%
503
 
1.5%
50 384
 
1.1%
3440 336
 
1.0%
level 323
 
0.9%
sea 323
 
0.9%
54 313
 
0.9%
80 302
 
0.9%
Other values (758) 15653
45.4%
2025-01-07T12:10:04.877946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18542
17.8%
0 15801
15.2%
m 8238
 
7.9%
t 7837
 
7.5%
f 7463
 
7.2%
1 5477
 
5.3%
5 4884
 
4.7%
4 4548
 
4.4%
3 4547
 
4.4%
2 4229
 
4.1%
Other values (37) 22398
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18542
17.8%
0 15801
15.2%
m 8238
 
7.9%
t 7837
 
7.5%
f 7463
 
7.2%
1 5477
 
5.3%
5 4884
 
4.7%
4 4548
 
4.4%
3 4547
 
4.4%
2 4229
 
4.1%
Other values (37) 22398
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18542
17.8%
0 15801
15.2%
m 8238
 
7.9%
t 7837
 
7.5%
f 7463
 
7.2%
1 5477
 
5.3%
5 4884
 
4.7%
4 4548
 
4.4%
3 4547
 
4.4%
2 4229
 
4.1%
Other values (37) 22398
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18542
17.8%
0 15801
15.2%
m 8238
 
7.9%
t 7837
 
7.5%
f 7463
 
7.2%
1 5477
 
5.3%
5 4884
 
4.7%
4 4548
 
4.4%
3 4547
 
4.4%
2 4229
 
4.1%
Other values (37) 22398
21.5%

verticalDatum
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

verbatimDepth
Text

Missing 

Distinct59
Distinct (%)4.0%
Missing336615
Missing (%)99.6%
Memory size2.6 MiB
2025-01-07T12:10:04.980457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length91
Median length10
Mean length8.625422583
Min length2

Characters and Unicode

Total characters12757
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)2.0%

Sample

1st rowto 1 m
2nd rowintertidal
3rd row<0.5 m
4th rowintertidal
5th rowintertidal
ValueCountFrequency (%)
intertidal 778
40.5%
m 259
 
13.5%
surface 253
 
13.2%
to 103
 
5.4%
1 95
 
4.9%
0-1 84
 
4.4%
intertida 84
 
4.4%
0.5 68
 
3.5%
1m 47
 
2.4%
cm 13
 
0.7%
Other values (55) 138
 
7.2%
2025-01-07T12:10:05.143439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1871
14.7%
i 1380
10.8%
e 1167
9.1%
a 1150
9.0%
r 1147
9.0%
n 891
 
7.0%
d 877
 
6.9%
l 806
 
6.3%
443
 
3.5%
I 353
 
2.8%
Other values (41) 2672
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1871
14.7%
i 1380
10.8%
e 1167
9.1%
a 1150
9.0%
r 1147
9.0%
n 891
 
7.0%
d 877
 
6.9%
l 806
 
6.3%
443
 
3.5%
I 353
 
2.8%
Other values (41) 2672
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1871
14.7%
i 1380
10.8%
e 1167
9.1%
a 1150
9.0%
r 1147
9.0%
n 891
 
7.0%
d 877
 
6.9%
l 806
 
6.3%
443
 
3.5%
I 353
 
2.8%
Other values (41) 2672
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1871
14.7%
i 1380
10.8%
e 1167
9.1%
a 1150
9.0%
r 1147
9.0%
n 891
 
7.0%
d 877
 
6.9%
l 806
 
6.3%
443
 
3.5%
I 353
 
2.8%
Other values (41) 2672
20.9%
Distinct2
Distinct (%)100.0%
Missing338092
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:05.212034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length30.5
Mean length30.5
Min length21

Characters and Unicode

Total characters61
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowCarpenter, Kent E.; Williams, Jeffrey T.
2nd rowKirkbride, J. H., Jr.
ValueCountFrequency (%)
carpenter 1
10.0%
kent 1
10.0%
e 1
10.0%
williams 1
10.0%
jeffrey 1
10.0%
t 1
10.0%
kirkbride 1
10.0%
j 1
10.0%
h 1
10.0%
jr 1
10.0%
2025-01-07T12:10:05.336950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
13.1%
r 6
 
9.8%
e 6
 
9.8%
. 5
 
8.2%
i 4
 
6.6%
, 4
 
6.6%
J 3
 
4.9%
K 2
 
3.3%
t 2
 
3.3%
a 2
 
3.3%
Other values (16) 19
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8
13.1%
r 6
 
9.8%
e 6
 
9.8%
. 5
 
8.2%
i 4
 
6.6%
, 4
 
6.6%
J 3
 
4.9%
K 2
 
3.3%
t 2
 
3.3%
a 2
 
3.3%
Other values (16) 19
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8
13.1%
r 6
 
9.8%
e 6
 
9.8%
. 5
 
8.2%
i 4
 
6.6%
, 4
 
6.6%
J 3
 
4.9%
K 2
 
3.3%
t 2
 
3.3%
a 2
 
3.3%
Other values (16) 19
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8
13.1%
r 6
 
9.8%
e 6
 
9.8%
. 5
 
8.2%
i 4
 
6.6%
, 4
 
6.6%
J 3
 
4.9%
K 2
 
3.3%
t 2
 
3.3%
a 2
 
3.3%
Other values (16) 19
31.1%

maximumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

locationAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

locationRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

decimalLatitude
Real number (ℝ)

Missing 

Distinct22660
Distinct (%)8.6%
Missing73462
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean18.75851192
Minimum-69.28
Maximum75
Zeros2
Zeros (%)< 0.1%
Negative52864
Negative (%)15.6%
Memory size2.6 MiB
2025-01-07T12:10:05.407108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-69.28
5-th percentile-22
Q17.0186425
median26.8463
Q336.0906
95-th percentile43.961
Maximum75
Range144.28
Interquartile range (IQR)29.0719575

Descriptive statistics

Standard deviation21.99468159
Coefficient of variation (CV)1.172517398
Kurtosis-0.1892834917
Mean18.75851192
Median Absolute Deviation (MAD)12.05185
Skewness-0.8202898879
Sum4964102.525
Variance483.7660182
MonotonicityNot monotonic
2025-01-07T12:10:05.474584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0832 1365
 
0.4%
16.802 1085
 
0.3%
31.7306 892
 
0.3%
-22 890
 
0.3%
5 791
 
0.2%
-17.4726 765
 
0.2%
38.6141 726
 
0.2%
34.9606 681
 
0.2%
-17.4825 679
 
0.2%
-9.82436 665
 
0.2%
Other values (22650) 256093
75.7%
(Missing) 73462
 
21.7%
ValueCountFrequency (%)
-69.28 10
 
< 0.1%
-69.067 1
 
< 0.1%
-68.3 22
< 0.1%
-68.17 36
< 0.1%
-68.15 28
< 0.1%
ValueCountFrequency (%)
75 15
< 0.1%
72.3787 17
< 0.1%
72.3394 3
 
< 0.1%
72.3157 1
 
< 0.1%
72.2473 1
 
< 0.1%

decimalLongitude
Real number (ℝ)

Missing 

Distinct21514
Distinct (%)8.1%
Missing73462
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean-64.67591475
Minimum-179.933
Maximum179.954
Zeros2
Zeros (%)< 0.1%
Negative217262
Negative (%)64.3%
Memory size2.6 MiB
2025-01-07T12:10:05.555272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-179.933
5-th percentile-149.883
Q1-110.44925
median-81.9746
Q3-65.9694
95-th percentile122.563
Maximum179.954
Range359.887
Interquartile range (IQR)44.47985

Descriptive statistics

Standard deviation78.68663976
Coefficient of variation (CV)-1.216629715
Kurtosis1.270141139
Mean-64.67591475
Median Absolute Deviation (MAD)23.8629
Skewness1.451272399
Sum-17115316.67
Variance6191.587276
MonotonicityNot monotonic
2025-01-07T12:10:05.679034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-68.8991 1347
 
0.4%
-56.1167 1222
 
0.4%
-149.826 1218
 
0.4%
-88.082 1101
 
0.3%
-149.775 1056
 
0.3%
-110.881 910
 
0.3%
-88.0817 835
 
0.2%
-80.2986 742
 
0.2%
-90.2589 731
 
0.2%
-176 682
 
0.2%
Other values (21504) 254788
75.4%
(Missing) 73462
 
21.7%
ValueCountFrequency (%)
-179.933 2
< 0.1%
-179.582 2
< 0.1%
-179.563 1
< 0.1%
-179.542 1
< 0.1%
-179.528 1
< 0.1%
ValueCountFrequency (%)
179.954 1
< 0.1%
179.878 1
< 0.1%
179.853 1
< 0.1%
179.84 1
< 0.1%
179.78 1
< 0.1%

coordinateUncertaintyInMeters
Real number (ℝ)

Missing 

Distinct453
Distinct (%)4.1%
Missing327083
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean7031.926985
Minimum3
Maximum534670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:05.771618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q140
median218.87
Q31266
95-th percentile24246
Maximum534670
Range534667
Interquartile range (IQR)1226

Descriptive statistics

Standard deviation34262.65772
Coefficient of variation (CV)4.87244219
Kurtosis65.76549808
Mean7031.926985
Median Absolute Deviation (MAD)206.87
Skewness7.745848496
Sum77428548.03
Variance1173929714
MonotonicityNot monotonic
2025-01-07T12:10:05.852693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1571
 
0.5%
5 435
 
0.1%
14 400
 
0.1%
12 386
 
0.1%
500 365
 
0.1%
10 311
 
0.1%
32 277
 
0.1%
200 273
 
0.1%
15 255
 
0.1%
23 231
 
0.1%
Other values (443) 6507
 
1.9%
(Missing) 327083
96.7%
ValueCountFrequency (%)
3 3
 
< 0.1%
4 16
 
< 0.1%
5 435
0.1%
6 44
 
< 0.1%
9 42
 
< 0.1%
ValueCountFrequency (%)
534670 1
 
< 0.1%
477476 2
 
< 0.1%
435450 4
 
< 0.1%
291123 20
 
< 0.1%
278076 108
< 0.1%

coordinatePrecision
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

pointRadiusSpatialFit
Real number (ℝ)

Missing 

Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2682943.25
Minimum2387143
Maximum2974262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:05.912665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2387143
5-th percentile2398590.7
Q12444381.5
median2685184
Q32923745.75
95-th percentile2964158.75
Maximum2974262
Range587119
Interquartile range (IQR)479364.25

Descriptive statistics

Standard deviation300386.7626
Coefficient of variation (CV)0.1119616535
Kurtosis-5.436808163
Mean2682943.25
Median Absolute Deviation (MAD)255400.5
Skewness-0.01224056835
Sum10731773
Variance9.023220713 × 1010
MonotonicityNot monotonic
2025-01-07T12:10:05.966097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2387143 1
 
< 0.1%
2906907 1
 
< 0.1%
2463461 1
 
< 0.1%
2974262 1
 
< 0.1%
(Missing) 338090
> 99.9%
ValueCountFrequency (%)
2387143 1
< 0.1%
2463461 1
< 0.1%
2906907 1
< 0.1%
2974262 1
< 0.1%
ValueCountFrequency (%)
2974262 1
< 0.1%
2906907 1
< 0.1%
2463461 1
< 0.1%
2387143 1
< 0.1%
Distinct6
Distinct (%)0.1%
Missing329029
Missing (%)97.3%
Memory size2.6 MiB
2025-01-07T12:10:06.012480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.7463872
Min length3

Characters and Unicode

Total characters206196
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 8918
33.1%
minutes 8843
32.8%
seconds 8843
32.8%
township 107
 
0.4%
range 107
 
0.4%
decimal 75
 
0.3%
utm 24
 
0.1%
unknown 16
 
0.1%
2025-01-07T12:10:06.139733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 44622
21.6%
s 26711
13.0%
n 17948
 
8.7%
17868
 
8.7%
i 9025
 
4.4%
g 9025
 
4.4%
D 8982
 
4.4%
o 8966
 
4.3%
r 8918
 
4.3%
c 8918
 
4.3%
Other values (15) 45213
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 206196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 44622
21.6%
s 26711
13.0%
n 17948
 
8.7%
17868
 
8.7%
i 9025
 
4.4%
g 9025
 
4.4%
D 8982
 
4.4%
o 8966
 
4.3%
r 8918
 
4.3%
c 8918
 
4.3%
Other values (15) 45213
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 206196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 44622
21.6%
s 26711
13.0%
n 17948
 
8.7%
17868
 
8.7%
i 9025
 
4.4%
g 9025
 
4.4%
D 8982
 
4.4%
o 8966
 
4.3%
r 8918
 
4.3%
c 8918
 
4.3%
Other values (15) 45213
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 206196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 44622
21.6%
s 26711
13.0%
n 17948
 
8.7%
17868
 
8.7%
i 9025
 
4.4%
g 9025
 
4.4%
D 8982
 
4.4%
o 8966
 
4.3%
r 8918
 
4.3%
c 8918
 
4.3%
Other values (15) 45213
21.9%

verbatimSRS
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

footprintWKT
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

footprintSRS
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

footprintSpatialFit
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

georeferencedBy
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:06.213912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length35
Mean length32.25
Min length19

Characters and Unicode

Total characters129
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowChampsodon nudivittis (Ogilby, 1895)
2nd rowCoccocypselum guianense (Aubl.) K.Schum.
3rd rowEmoia caeruleocauda (De Vis, 1892)
4th rowDimorphandra Schott
ValueCountFrequency (%)
champsodon 1
 
6.7%
nudivittis 1
 
6.7%
ogilby 1
 
6.7%
1895 1
 
6.7%
coccocypselum 1
 
6.7%
guianense 1
 
6.7%
aubl 1
 
6.7%
k.schum 1
 
6.7%
emoia 1
 
6.7%
caeruleocauda 1
 
6.7%
Other values (5) 5
33.3%
2025-01-07T12:10:06.345022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
8.5%
o 8
 
6.2%
i 8
 
6.2%
a 8
 
6.2%
u 7
 
5.4%
c 7
 
5.4%
e 6
 
4.7%
s 5
 
3.9%
m 5
 
3.9%
n 5
 
3.9%
Other values (27) 59
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11
 
8.5%
o 8
 
6.2%
i 8
 
6.2%
a 8
 
6.2%
u 7
 
5.4%
c 7
 
5.4%
e 6
 
4.7%
s 5
 
3.9%
m 5
 
3.9%
n 5
 
3.9%
Other values (27) 59
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11
 
8.5%
o 8
 
6.2%
i 8
 
6.2%
a 8
 
6.2%
u 7
 
5.4%
c 7
 
5.4%
e 6
 
4.7%
s 5
 
3.9%
m 5
 
3.9%
n 5
 
3.9%
Other values (27) 59
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11
 
8.5%
o 8
 
6.2%
i 8
 
6.2%
a 8
 
6.2%
u 7
 
5.4%
c 7
 
5.4%
e 6
 
4.7%
s 5
 
3.9%
m 5
 
3.9%
n 5
 
3.9%
Other values (27) 59
45.7%

georeferencedDate
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

georeferenceProtocol
Text

Missing 

Distinct172
Distinct (%)0.2%
Missing255273
Missing (%)75.5%
Memory size2.6 MiB
2025-01-07T12:10:06.555211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length228
Median length12
Mean length16.00842781
Min length3

Characters and Unicode

Total characters1325834
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowGoogle Earth
2nd rowGoogle Earth
3rd rowGoogle Earth
4th rowGeoLocate
5th rowGoogle Earth
ValueCountFrequency (%)
google 50688
24.4%
earth 44693
21.5%
gps 24170
 
11.6%
maps 6421
 
3.1%
georeferencing 4994
 
2.4%
and 3621
 
1.7%
pro 3250
 
1.6%
for 3177
 
1.5%
to 3177
 
1.5%
wieczorek 3176
 
1.5%
Other values (336) 60464
29.1%
2025-01-07T12:10:06.853294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 139882
 
10.6%
125010
 
9.4%
e 116797
 
8.8%
r 91824
 
6.9%
G 90277
 
6.8%
a 86798
 
6.5%
t 72957
 
5.5%
g 60276
 
4.5%
l 58288
 
4.4%
h 52448
 
4.0%
Other values (59) 431277
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1325834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 139882
 
10.6%
125010
 
9.4%
e 116797
 
8.8%
r 91824
 
6.9%
G 90277
 
6.8%
a 86798
 
6.5%
t 72957
 
5.5%
g 60276
 
4.5%
l 58288
 
4.4%
h 52448
 
4.0%
Other values (59) 431277
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1325834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 139882
 
10.6%
125010
 
9.4%
e 116797
 
8.8%
r 91824
 
6.9%
G 90277
 
6.8%
a 86798
 
6.5%
t 72957
 
5.5%
g 60276
 
4.5%
l 58288
 
4.4%
h 52448
 
4.0%
Other values (59) 431277
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1325834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 139882
 
10.6%
125010
 
9.4%
e 116797
 
8.8%
r 91824
 
6.9%
G 90277
 
6.8%
a 86798
 
6.5%
t 72957
 
5.5%
g 60276
 
4.5%
l 58288
 
4.4%
h 52448
 
4.0%
Other values (59) 431277
32.5%

georeferenceSources
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

georeferenceRemarks
Text

Missing 

Distinct224
Distinct (%)2.4%
Missing328595
Missing (%)97.2%
Memory size2.6 MiB
2025-01-07T12:10:07.170108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length83
Median length51
Mean length18.53163491
Min length2

Characters and Unicode

Total characters176032
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.2%

Sample

1st rowMax error (m): 100
2nd rowMax error (m): 40
3rd rowLocality extent = 1.6
4th rowLocality extent = 1 mile
5th rowMax error (m): 200
ValueCountFrequency (%)
m 5353
14.5%
max 4966
13.5%
error 4966
13.5%
1990
 
5.4%
locality 1819
 
4.9%
extent 1818
 
4.9%
100 1765
 
4.8%
50 914
 
2.5%
200 739
 
2.0%
4 668
 
1.8%
Other values (241) 11820
32.1%
2025-01-07T12:10:07.624771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27319
15.5%
r 16672
 
9.5%
e 10647
 
6.0%
o 10229
 
5.8%
a 10113
 
5.7%
t 9606
 
5.5%
0 8337
 
4.7%
x 7001
 
4.0%
m 6536
 
3.7%
n 5377
 
3.1%
Other values (53) 64195
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27319
15.5%
r 16672
 
9.5%
e 10647
 
6.0%
o 10229
 
5.8%
a 10113
 
5.7%
t 9606
 
5.5%
0 8337
 
4.7%
x 7001
 
4.0%
m 6536
 
3.7%
n 5377
 
3.1%
Other values (53) 64195
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27319
15.5%
r 16672
 
9.5%
e 10647
 
6.0%
o 10229
 
5.8%
a 10113
 
5.7%
t 9606
 
5.5%
0 8337
 
4.7%
x 7001
 
4.0%
m 6536
 
3.7%
n 5377
 
3.1%
Other values (53) 64195
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27319
15.5%
r 16672
 
9.5%
e 10647
 
6.0%
o 10229
 
5.8%
a 10113
 
5.7%
t 9606
 
5.5%
0 8337
 
4.7%
x 7001
 
4.0%
m 6536
 
3.7%
n 5377
 
3.1%
Other values (53) 64195
36.5%

geologicalContextID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

earliestEonOrLowestEonothem
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:07.725287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length134
Median length71
Mean length83.5
Min length58

Characters and Unicode

Total characters334
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowAnimalia, Chordata, Vertebrata, Osteichthyes, Actinopterygii, Neopterygii, Acanthopterygii, Perciformes, Trachinoidei, Champsodontidae
2nd rowPlantae, Dicotyledonae, Gentianales, Rubiaceae, Rubioideae
3rd rowAnimalia, Chordata, Vertebrata, Reptilia, Squamata, Sauria, Scincidae, Eugongylinae
4th rowPlantae, Dicotyledonae, Fabales, Fabaceae, Caesalpinioideae
ValueCountFrequency (%)
animalia 2
 
7.1%
chordata 2
 
7.1%
vertebrata 2
 
7.1%
dicotyledonae 2
 
7.1%
plantae 2
 
7.1%
osteichthyes 1
 
3.6%
actinopterygii 1
 
3.6%
acanthopterygii 1
 
3.6%
neopterygii 1
 
3.6%
trachinoidei 1
 
3.6%
Other values (13) 13
46.4%
2025-01-07T12:10:07.878608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 43
12.9%
e 35
 
10.5%
i 32
 
9.6%
24
 
7.2%
, 24
 
7.2%
t 21
 
6.3%
n 16
 
4.8%
o 16
 
4.8%
r 13
 
3.9%
c 11
 
3.3%
Other values (25) 99
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 43
12.9%
e 35
 
10.5%
i 32
 
9.6%
24
 
7.2%
, 24
 
7.2%
t 21
 
6.3%
n 16
 
4.8%
o 16
 
4.8%
r 13
 
3.9%
c 11
 
3.3%
Other values (25) 99
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 43
12.9%
e 35
 
10.5%
i 32
 
9.6%
24
 
7.2%
, 24
 
7.2%
t 21
 
6.3%
n 16
 
4.8%
o 16
 
4.8%
r 13
 
3.9%
c 11
 
3.3%
Other values (25) 99
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 43
12.9%
e 35
 
10.5%
i 32
 
9.6%
24
 
7.2%
, 24
 
7.2%
t 21
 
6.3%
n 16
 
4.8%
o 16
 
4.8%
r 13
 
3.9%
c 11
 
3.3%
Other values (25) 99
29.6%
Distinct2
Distinct (%)50.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:07.931178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7.5
Mean length7.5
Min length7

Characters and Unicode

Total characters30
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimalia
2nd rowPlantae
3rd rowAnimalia
4th rowPlantae
ValueCountFrequency (%)
animalia 2
50.0%
plantae 2
50.0%
2025-01-07T12:10:08.036933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8
26.7%
i 4
13.3%
n 4
13.3%
l 4
13.3%
A 2
 
6.7%
m 2
 
6.7%
P 2
 
6.7%
t 2
 
6.7%
e 2
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8
26.7%
i 4
13.3%
n 4
13.3%
l 4
13.3%
A 2
 
6.7%
m 2
 
6.7%
P 2
 
6.7%
t 2
 
6.7%
e 2
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8
26.7%
i 4
13.3%
n 4
13.3%
l 4
13.3%
A 2
 
6.7%
m 2
 
6.7%
P 2
 
6.7%
t 2
 
6.7%
e 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8
26.7%
i 4
13.3%
n 4
13.3%
l 4
13.3%
A 2
 
6.7%
m 2
 
6.7%
P 2
 
6.7%
t 2
 
6.7%
e 2
 
6.7%
Distinct2
Distinct (%)50.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:08.091797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10
Min length8

Characters and Unicode

Total characters40
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChordata
2nd rowTracheophyta
3rd rowChordata
4th rowTracheophyta
ValueCountFrequency (%)
chordata 2
50.0%
tracheophyta 2
50.0%
2025-01-07T12:10:08.209428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8
20.0%
h 6
15.0%
r 4
10.0%
o 4
10.0%
t 4
10.0%
C 2
 
5.0%
d 2
 
5.0%
T 2
 
5.0%
c 2
 
5.0%
e 2
 
5.0%
Other values (2) 4
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8
20.0%
h 6
15.0%
r 4
10.0%
o 4
10.0%
t 4
10.0%
C 2
 
5.0%
d 2
 
5.0%
T 2
 
5.0%
c 2
 
5.0%
e 2
 
5.0%
Other values (2) 4
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8
20.0%
h 6
15.0%
r 4
10.0%
o 4
10.0%
t 4
10.0%
C 2
 
5.0%
d 2
 
5.0%
T 2
 
5.0%
c 2
 
5.0%
e 2
 
5.0%
Other values (2) 4
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8
20.0%
h 6
15.0%
r 4
10.0%
o 4
10.0%
t 4
10.0%
C 2
 
5.0%
d 2
 
5.0%
T 2
 
5.0%
c 2
 
5.0%
e 2
 
5.0%
Other values (2) 4
10.0%
Distinct2
Distinct (%)66.7%
Missing338091
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:08.259140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.33333333
Min length8

Characters and Unicode

Total characters34
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowMagnoliopsida
2nd rowSquamata
3rd rowMagnoliopsida
ValueCountFrequency (%)
magnoliopsida 2
66.7%
squamata 1
33.3%
2025-01-07T12:10:08.367103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7
20.6%
i 4
11.8%
o 4
11.8%
g 2
 
5.9%
M 2
 
5.9%
n 2
 
5.9%
l 2
 
5.9%
p 2
 
5.9%
s 2
 
5.9%
d 2
 
5.9%
Other values (5) 5
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7
20.6%
i 4
11.8%
o 4
11.8%
g 2
 
5.9%
M 2
 
5.9%
n 2
 
5.9%
l 2
 
5.9%
p 2
 
5.9%
s 2
 
5.9%
d 2
 
5.9%
Other values (5) 5
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7
20.6%
i 4
11.8%
o 4
11.8%
g 2
 
5.9%
M 2
 
5.9%
n 2
 
5.9%
l 2
 
5.9%
p 2
 
5.9%
s 2
 
5.9%
d 2
 
5.9%
Other values (5) 5
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7
20.6%
i 4
11.8%
o 4
11.8%
g 2
 
5.9%
M 2
 
5.9%
n 2
 
5.9%
l 2
 
5.9%
p 2
 
5.9%
s 2
 
5.9%
d 2
 
5.9%
Other values (5) 5
14.7%
Distinct3
Distinct (%)100.0%
Missing338091
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:08.428863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.666666667
Min length7

Characters and Unicode

Total characters29
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowPerciformes
2nd rowGentianales
3rd rowFabales
ValueCountFrequency (%)
perciformes 1
33.3%
gentianales 1
33.3%
fabales 1
33.3%
2025-01-07T12:10:08.560348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5
17.2%
a 4
13.8%
s 3
10.3%
r 2
 
6.9%
n 2
 
6.9%
l 2
 
6.9%
i 2
 
6.9%
P 1
 
3.4%
m 1
 
3.4%
o 1
 
3.4%
Other values (6) 6
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5
17.2%
a 4
13.8%
s 3
10.3%
r 2
 
6.9%
n 2
 
6.9%
l 2
 
6.9%
i 2
 
6.9%
P 1
 
3.4%
m 1
 
3.4%
o 1
 
3.4%
Other values (6) 6
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5
17.2%
a 4
13.8%
s 3
10.3%
r 2
 
6.9%
n 2
 
6.9%
l 2
 
6.9%
i 2
 
6.9%
P 1
 
3.4%
m 1
 
3.4%
o 1
 
3.4%
Other values (6) 6
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5
17.2%
a 4
13.8%
s 3
10.3%
r 2
 
6.9%
n 2
 
6.9%
l 2
 
6.9%
i 2
 
6.9%
P 1
 
3.4%
m 1
 
3.4%
o 1
 
3.4%
Other values (6) 6
20.7%

earliestEpochOrLowestSeries
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:08.618726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length12
Mean length10.25
Min length8

Characters and Unicode

Total characters41
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowChampsodontidae
2nd rowRubiaceae
3rd rowScincidae
4th rowFabaceae
ValueCountFrequency (%)
champsodontidae 1
25.0%
rubiaceae 1
25.0%
scincidae 1
25.0%
fabaceae 1
25.0%
2025-01-07T12:10:08.739038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8
19.5%
e 6
14.6%
i 4
9.8%
c 4
9.8%
d 3
 
7.3%
b 2
 
4.9%
o 2
 
4.9%
n 2
 
4.9%
h 1
 
2.4%
C 1
 
2.4%
Other values (8) 8
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8
19.5%
e 6
14.6%
i 4
9.8%
c 4
9.8%
d 3
 
7.3%
b 2
 
4.9%
o 2
 
4.9%
n 2
 
4.9%
h 1
 
2.4%
C 1
 
2.4%
Other values (8) 8
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8
19.5%
e 6
14.6%
i 4
9.8%
c 4
9.8%
d 3
 
7.3%
b 2
 
4.9%
o 2
 
4.9%
n 2
 
4.9%
h 1
 
2.4%
C 1
 
2.4%
Other values (8) 8
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8
19.5%
e 6
14.6%
i 4
9.8%
c 4
9.8%
d 3
 
7.3%
b 2
 
4.9%
o 2
 
4.9%
n 2
 
4.9%
h 1
 
2.4%
C 1
 
2.4%
Other values (8) 8
19.5%

earliestAgeOrLowestStage
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

latestAgeOrHighestStage
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

lowestBiostratigraphicZone
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:08.793840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10
Min length5

Characters and Unicode

Total characters40
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowChampsodon
2nd rowCoccocypselum
3rd rowEmoia
4th rowDimorphandra
ValueCountFrequency (%)
champsodon 1
25.0%
coccocypselum 1
25.0%
emoia 1
25.0%
dimorphandra 1
25.0%
2025-01-07T12:10:08.916061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%
Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:08.969781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10
Min length5

Characters and Unicode

Total characters40
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowChampsodon
2nd rowCoccocypselum
3rd rowEmoia
4th rowDimorphandra
ValueCountFrequency (%)
champsodon 1
25.0%
coccocypselum 1
25.0%
emoia 1
25.0%
dimorphandra 1
25.0%
2025-01-07T12:10:09.084710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6
15.0%
a 4
 
10.0%
m 4
 
10.0%
p 3
 
7.5%
c 3
 
7.5%
C 2
 
5.0%
h 2
 
5.0%
s 2
 
5.0%
d 2
 
5.0%
n 2
 
5.0%
Other values (8) 10
25.0%

group
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

formation
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

member
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing338091
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:09.135621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length10.66666667
Min length9

Characters and Unicode

Total characters32
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rownudivittis
2nd rowguianense
3rd rowcaeruleocauda
ValueCountFrequency (%)
nudivittis 1
33.3%
guianense 1
33.3%
caeruleocauda 1
33.3%
2025-01-07T12:10:09.248990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 4
12.5%
i 4
12.5%
a 4
12.5%
e 4
12.5%
n 3
9.4%
d 2
6.2%
s 2
6.2%
t 2
6.2%
c 2
6.2%
v 1
 
3.1%
Other values (4) 4
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 4
12.5%
i 4
12.5%
a 4
12.5%
e 4
12.5%
n 3
9.4%
d 2
6.2%
s 2
6.2%
t 2
6.2%
c 2
6.2%
v 1
 
3.1%
Other values (4) 4
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 4
12.5%
i 4
12.5%
a 4
12.5%
e 4
12.5%
n 3
9.4%
d 2
6.2%
s 2
6.2%
t 2
6.2%
c 2
6.2%
v 1
 
3.1%
Other values (4) 4
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 4
12.5%
i 4
12.5%
a 4
12.5%
e 4
12.5%
n 3
9.4%
d 2
6.2%
s 2
6.2%
t 2
6.2%
c 2
6.2%
v 1
 
3.1%
Other values (4) 4
12.5%

bed
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

identificationID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct2
Distinct (%)50.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:09.293744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.5
Min length5

Characters and Unicode

Total characters26
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowSPECIES
2nd rowSPECIES
3rd rowSPECIES
4th rowGENUS
ValueCountFrequency (%)
species 3
75.0%
genus 1
 
25.0%
2025-01-07T12:10:09.401351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 7
26.9%
E 7
26.9%
P 3
11.5%
C 3
11.5%
I 3
11.5%
G 1
 
3.8%
N 1
 
3.8%
U 1
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 7
26.9%
E 7
26.9%
P 3
11.5%
C 3
11.5%
I 3
11.5%
G 1
 
3.8%
N 1
 
3.8%
U 1
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 7
26.9%
E 7
26.9%
P 3
11.5%
C 3
11.5%
I 3
11.5%
G 1
 
3.8%
N 1
 
3.8%
U 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 7
26.9%
E 7
26.9%
P 3
11.5%
C 3
11.5%
I 3
11.5%
G 1
 
3.8%
N 1
 
3.8%
U 1
 
3.8%
Distinct16
Distinct (%)0.3%
Missing333028
Missing (%)98.5%
Memory size2.6 MiB
2025-01-07T12:10:09.452898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.255823135
Min length2

Characters and Unicode

Total characters26626
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaff.
2nd rowcf.
3rd rowaff.
4th rowuncertain
5th rowuncertain
ValueCountFrequency (%)
cf 2738
53.8%
uncertain 1858
36.5%
aff 320
 
6.3%
near 75
 
1.5%
complex 38
 
0.7%
sp 16
 
0.3%
group 12
 
0.2%
n 10
 
0.2%
nov 6
 
0.1%
vel 5
 
0.1%
Other values (2) 9
 
0.2%
2025-01-07T12:10:09.565779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 4628
17.4%
n 3807
14.3%
f 3378
12.7%
. 2728
10.2%
a 2237
8.4%
e 1976
7.4%
r 1945
7.3%
t 1858
7.0%
i 1858
7.0%
u 1842
 
6.9%
Other values (12) 369
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 4628
17.4%
n 3807
14.3%
f 3378
12.7%
. 2728
10.2%
a 2237
8.4%
e 1976
7.4%
r 1945
7.3%
t 1858
7.0%
i 1858
7.0%
u 1842
 
6.9%
Other values (12) 369
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 4628
17.4%
n 3807
14.3%
f 3378
12.7%
. 2728
10.2%
a 2237
8.4%
e 1976
7.4%
r 1945
7.3%
t 1858
7.0%
i 1858
7.0%
u 1842
 
6.9%
Other values (12) 369
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 4628
17.4%
n 3807
14.3%
f 3378
12.7%
. 2728
10.2%
a 2237
8.4%
e 1976
7.4%
r 1945
7.3%
t 1858
7.0%
i 1858
7.0%
u 1842
 
6.9%
Other values (12) 369
 
1.4%

typeStatus
Text

Missing 

Distinct10
Distinct (%)0.2%
Missing331537
Missing (%)98.1%
Memory size2.6 MiB
2025-01-07T12:10:09.615937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.010370596
Min length4

Characters and Unicode

Total characters52524
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARATYPE
2nd rowPARATYPE
3rd rowPARATYPE
4th rowPARATYPE
5th rowPARATYPE
ValueCountFrequency (%)
paratype 5817
88.7%
holotype 330
 
5.0%
paralectotype 125
 
1.9%
cotype 86
 
1.3%
syntype 76
 
1.2%
type 73
 
1.1%
allotype 23
 
0.4%
neotype 13
 
0.2%
topotype 10
 
0.2%
isotype 4
 
0.1%
2025-01-07T12:10:09.731514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 12509
23.8%
A 11907
22.7%
E 6695
12.7%
T 6692
12.7%
Y 6633
12.6%
R 5942
11.3%
O 931
 
1.8%
L 501
 
1.0%
H 330
 
0.6%
C 211
 
0.4%
Other values (3) 173
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 12509
23.8%
A 11907
22.7%
E 6695
12.7%
T 6692
12.7%
Y 6633
12.6%
R 5942
11.3%
O 931
 
1.8%
L 501
 
1.0%
H 330
 
0.6%
C 211
 
0.4%
Other values (3) 173
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 12509
23.8%
A 11907
22.7%
E 6695
12.7%
T 6692
12.7%
Y 6633
12.6%
R 5942
11.3%
O 931
 
1.8%
L 501
 
1.0%
H 330
 
0.6%
C 211
 
0.4%
Other values (3) 173
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 12509
23.8%
A 11907
22.7%
E 6695
12.7%
T 6692
12.7%
Y 6633
12.6%
R 5942
11.3%
O 931
 
1.8%
L 501
 
1.0%
H 330
 
0.6%
C 211
 
0.4%
Other values (3) 173
 
0.3%

identifiedBy
Text

Missing 

Distinct1866
Distinct (%)1.7%
Missing226045
Missing (%)66.9%
Memory size2.6 MiB
2025-01-07T12:10:09.927476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length150
Median length128
Mean length39.12826531
Min length2

Characters and Unicode

Total characters4384283
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)0.2%

Sample

1st rowAnker, Arthur
2nd rowOsborn, Karen J., (IZ), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
3rd rowBaldwin, Carole C.
4th rowHobbs, Horton H., Jr., Smithsonian Institution, National Museum of Natural History
5th rowPaulay, Gustav, University of Florida (UNITED STATES)
ValueCountFrequency (%)
united 36040
 
5.8%
states 35997
 
5.8%
of 27839
 
4.5%
smithsonian 24345
 
3.9%
22476
 
3.6%
institution 20514
 
3.3%
national 18658
 
3.0%
museum 17521
 
2.8%
natural 17241
 
2.8%
history 17162
 
2.8%
Other values (2280) 384193
61.8%
2025-01-07T12:10:10.219791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
509937
 
11.6%
i 263018
 
6.0%
a 259974
 
5.9%
t 236702
 
5.4%
n 236587
 
5.4%
o 217618
 
5.0%
e 199155
 
4.5%
, 179169
 
4.1%
r 173333
 
4.0%
s 169593
 
3.9%
Other values (73) 1939197
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4384283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
509937
 
11.6%
i 263018
 
6.0%
a 259974
 
5.9%
t 236702
 
5.4%
n 236587
 
5.4%
o 217618
 
5.0%
e 199155
 
4.5%
, 179169
 
4.1%
r 173333
 
4.0%
s 169593
 
3.9%
Other values (73) 1939197
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4384283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
509937
 
11.6%
i 263018
 
6.0%
a 259974
 
5.9%
t 236702
 
5.4%
n 236587
 
5.4%
o 217618
 
5.0%
e 199155
 
4.5%
, 179169
 
4.1%
r 173333
 
4.0%
s 169593
 
3.9%
Other values (73) 1939197
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4384283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
509937
 
11.6%
i 263018
 
6.0%
a 259974
 
5.9%
t 236702
 
5.4%
n 236587
 
5.4%
o 217618
 
5.0%
e 199155
 
4.5%
, 179169
 
4.1%
r 173333
 
4.0%
s 169593
 
3.9%
Other values (73) 1939197
44.2%

identifiedByID
Text

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:10.276775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACCEPTED
2nd rowACCEPTED
3rd rowACCEPTED
4th rowACCEPTED
ValueCountFrequency (%)
accepted 4
100.0%
2025-01-07T12:10:10.371536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 8
25.0%
E 8
25.0%
A 4
12.5%
P 4
12.5%
T 4
12.5%
D 4
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 8
25.0%
E 8
25.0%
A 4
12.5%
P 4
12.5%
T 4
12.5%
D 4
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 8
25.0%
E 8
25.0%
A 4
12.5%
P 4
12.5%
T 4
12.5%
D 4
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 8
25.0%
E 8
25.0%
A 4
12.5%
P 4
12.5%
T 4
12.5%
D 4
12.5%

dateIdentified
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

identificationReferences
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

identificationVerificationStatus
Text

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:10.427709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters144
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26098c25-8f7f-4c71-97ac-1d3db181c65e
2nd row26098c25-8f7f-4c71-97ac-1d3db181c65e
3rd row26098c25-8f7f-4c71-97ac-1d3db181c65e
4th row26098c25-8f7f-4c71-97ac-1d3db181c65e
ValueCountFrequency (%)
26098c25-8f7f-4c71-97ac-1d3db181c65e 4
100.0%
2025-01-07T12:10:10.531638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 16
11.1%
1 16
11.1%
c 16
11.1%
7 12
 
8.3%
8 12
 
8.3%
2 8
 
5.6%
9 8
 
5.6%
5 8
 
5.6%
6 8
 
5.6%
d 8
 
5.6%
Other values (7) 32
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 16
11.1%
1 16
11.1%
c 16
11.1%
7 12
 
8.3%
8 12
 
8.3%
2 8
 
5.6%
9 8
 
5.6%
5 8
 
5.6%
6 8
 
5.6%
d 8
 
5.6%
Other values (7) 32
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 16
11.1%
1 16
11.1%
c 16
11.1%
7 12
 
8.3%
8 12
 
8.3%
2 8
 
5.6%
9 8
 
5.6%
5 8
 
5.6%
6 8
 
5.6%
d 8
 
5.6%
Other values (7) 32
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 16
11.1%
1 16
11.1%
c 16
11.1%
7 12
 
8.3%
8 12
 
8.3%
2 8
 
5.6%
9 8
 
5.6%
5 8
 
5.6%
6 8
 
5.6%
d 8
 
5.6%
Other values (7) 32
22.2%

identificationRemarks
Text

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:10.574360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
ValueCountFrequency (%)
us 4
100.0%
2025-01-07T12:10:10.668995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4
50.0%
S 4
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4
50.0%
S 4
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4
50.0%
S 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4
50.0%
S 4
50.0%

taxonID
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:10.850553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters96
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row2024-12-01T12:07:17.508Z
2nd row2024-12-01T12:07:28.759Z
3rd row2024-12-01T12:07:38.231Z
4th row2024-12-01T12:07:36.611Z
ValueCountFrequency (%)
2024-12-01t12:07:17.508z 1
25.0%
2024-12-01t12:07:28.759z 1
25.0%
2024-12-01t12:07:38.231z 1
25.0%
2024-12-01t12:07:36.611z 1
25.0%
2025-01-07T12:10:10.967876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 18
18.8%
1 16
16.7%
0 13
13.5%
: 8
8.3%
- 8
8.3%
7 6
 
6.2%
4 4
 
4.2%
T 4
 
4.2%
. 4
 
4.2%
Z 4
 
4.2%
Other values (5) 11
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 18
18.8%
1 16
16.7%
0 13
13.5%
: 8
8.3%
- 8
8.3%
7 6
 
6.2%
4 4
 
4.2%
T 4
 
4.2%
. 4
 
4.2%
Z 4
 
4.2%
Other values (5) 11
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 18
18.8%
1 16
16.7%
0 13
13.5%
: 8
8.3%
- 8
8.3%
7 6
 
6.2%
4 4
 
4.2%
T 4
 
4.2%
. 4
 
4.2%
Z 4
 
4.2%
Other values (5) 11
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 18
18.8%
1 16
16.7%
0 13
13.5%
: 8
8.3%
- 8
8.3%
7 6
 
6.2%
4 4
 
4.2%
T 4
 
4.2%
. 4
 
4.2%
Z 4
 
4.2%
Other values (5) 11
11.5%

scientificNameID
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing338092
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean795.75
Minimum651
Maximum940.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:11.028644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile665.475
Q1723.375
median795.75
Q3868.125
95-th percentile926.025
Maximum940.5
Range289.5
Interquartile range (IQR)144.75

Descriptive statistics

Standard deviation204.7074132
Coefficient of variation (CV)0.2572509119
Kurtosisnan
Mean795.75
Median Absolute Deviation (MAD)144.75
Skewnessnan
Sum1591.5
Variance41905.125
MonotonicityStrictly decreasing
2025-01-07T12:10:11.078190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
940.5 1
 
< 0.1%
651 1
 
< 0.1%
(Missing) 338092
> 99.9%
ValueCountFrequency (%)
651 1
< 0.1%
940.5 1
< 0.1%
ValueCountFrequency (%)
940.5 1
< 0.1%
651 1
< 0.1%

acceptedNameUsageID
Real number (ℝ)

Missing 

Distinct44952
Distinct (%)13.5%
Missing6111
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean3675115.437
Minimum1
Maximum12386548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:11.139931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5220
Q11941299
median2431198
Q35209252
95-th percentile9512501
Maximum12386548
Range12386547
Interquartile range (IQR)3267953

Descriptive statistics

Standard deviation2798765.172
Coefficient of variation (CV)0.761544833
Kurtosis0.5025146025
Mean3675115.437
Median Absolute Deviation (MAD)685144
Skewness1.146368983
Sum1.220075848 × 1012
Variance7.833086487 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:11.218554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6841 3252
 
1.0%
637 2297
 
0.7%
2285664 2008
 
0.6%
2329589 2006
 
0.6%
2440447 1919
 
0.6%
8324617 1660
 
0.5%
8770992 1474
 
0.4%
2431491 1334
 
0.4%
2307333 1035
 
0.3%
68 875
 
0.3%
Other values (44942) 314123
92.9%
(Missing) 6111
 
1.8%
ValueCountFrequency (%)
1 236
0.1%
3 3
 
< 0.1%
5 9
 
< 0.1%
6 335
0.1%
19 24
 
< 0.1%
ValueCountFrequency (%)
12386548 6
< 0.1%
12385823 1
 
< 0.1%
12373983 1
 
< 0.1%
12356386 6
< 0.1%
12350721 5
< 0.1%

parentNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

originalNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

nameAccordingToID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

namePublishedInID
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:11.281377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length104
Median length92.5
Mean length60.25
Min length28

Characters and Unicode

Total characters241
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowGEODETIC_DATUM_ASSUMED_WGS84;GEODETIC_DATUM_INVALID;CONTINENT_DERIVED_FROM_COORDINATES;CONTINENT_INVALID
2nd rowGEODETIC_DATUM_ASSUMED_WGS84
3rd rowGEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES;CONTINENT_INVALID
4th rowGEODETIC_DATUM_ASSUMED_WGS84
ValueCountFrequency (%)
geodetic_datum_assumed_wgs84 2
50.0%
geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates;continent_invalid 1
25.0%
geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 1
25.0%
2025-01-07T12:10:11.406615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 24
 
10.0%
D 23
 
9.5%
_ 22
 
9.1%
T 20
 
8.3%
I 19
 
7.9%
N 17
 
7.1%
O 15
 
6.2%
S 14
 
5.8%
A 14
 
5.8%
M 11
 
4.6%
Other values (11) 62
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 24
 
10.0%
D 23
 
9.5%
_ 22
 
9.1%
T 20
 
8.3%
I 19
 
7.9%
N 17
 
7.1%
O 15
 
6.2%
S 14
 
5.8%
A 14
 
5.8%
M 11
 
4.6%
Other values (11) 62
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 24
 
10.0%
D 23
 
9.5%
_ 22
 
9.1%
T 20
 
8.3%
I 19
 
7.9%
N 17
 
7.1%
O 15
 
6.2%
S 14
 
5.8%
A 14
 
5.8%
M 11
 
4.6%
Other values (11) 62
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 24
 
10.0%
D 23
 
9.5%
_ 22
 
9.1%
T 20
 
8.3%
I 19
 
7.9%
N 17
 
7.1%
O 15
 
6.2%
S 14
 
5.8%
A 14
 
5.8%
M 11
 
4.6%
Other values (11) 62
25.7%

taxonConceptID
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct45747
Distinct (%)13.5%
Missing1
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:11.613628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length95
Mean length30.8157785
Min length4

Characters and Unicode

Total characters10418599
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9824 ?
Unique (%)2.9%

Sample

1st rowRectiostoma fernaldella
2nd rowSiboglinidae
3rd rowAmphinomidae
4th rowCambaridae
5th rowPolystichum Roth
ValueCountFrequency (%)
37261
 
3.0%
linnaeus 10150
 
0.8%
1758 7990
 
0.6%
l 6159
 
0.5%
incertae 6107
 
0.5%
sedis 6107
 
0.5%
1985 5118
 
0.4%
plethodon 4673
 
0.4%
orconectes 4548
 
0.4%
walker 4503
 
0.4%
Other values (49822) 1170241
92.7%
2025-01-07T12:10:11.921558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
924764
 
8.9%
a 838089
 
8.0%
e 687702
 
6.6%
i 638989
 
6.1%
r 539087
 
5.2%
s 536232
 
5.1%
o 513067
 
4.9%
n 469039
 
4.5%
l 423628
 
4.1%
t 373559
 
3.6%
Other values (97) 4474443
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10418599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
924764
 
8.9%
a 838089
 
8.0%
e 687702
 
6.6%
i 638989
 
6.1%
r 539087
 
5.2%
s 536232
 
5.1%
o 513067
 
4.9%
n 469039
 
4.5%
l 423628
 
4.1%
t 373559
 
3.6%
Other values (97) 4474443
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10418599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
924764
 
8.9%
a 838089
 
8.0%
e 687702
 
6.6%
i 638989
 
6.1%
r 539087
 
5.2%
s 536232
 
5.1%
o 513067
 
4.9%
n 469039
 
4.5%
l 423628
 
4.1%
t 373559
 
3.6%
Other values (97) 4474443
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10418599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
924764
 
8.9%
a 838089
 
8.0%
e 687702
 
6.6%
i 638989
 
6.1%
r 539087
 
5.2%
s 536232
 
5.1%
o 513067
 
4.9%
n 469039
 
4.5%
l 423628
 
4.1%
t 373559
 
3.6%
Other values (97) 4474443
42.9%

acceptedNameUsage
Boolean

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
False
 
4
(Missing)
338090 
ValueCountFrequency (%)
False 4
 
< 0.1%
(Missing) 338090
> 99.9%
2025-01-07T12:10:11.996678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

parentNameUsage
Real number (ℝ)

Missing 

Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2682943.25
Minimum2387143
Maximum2974262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:12.043621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2387143
5-th percentile2398590.7
Q12444381.5
median2685184
Q32923745.75
95-th percentile2964158.75
Maximum2974262
Range587119
Interquartile range (IQR)479364.25

Descriptive statistics

Standard deviation300386.7626
Coefficient of variation (CV)0.1119616535
Kurtosis-5.436808163
Mean2682943.25
Median Absolute Deviation (MAD)255400.5
Skewness-0.01224056835
Sum10731773
Variance9.023220713 × 1010
MonotonicityNot monotonic
2025-01-07T12:10:12.096618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2387143 1
 
< 0.1%
2906907 1
 
< 0.1%
2463461 1
 
< 0.1%
2974262 1
 
< 0.1%
(Missing) 338090
> 99.9%
ValueCountFrequency (%)
2387143 1
< 0.1%
2463461 1
< 0.1%
2906907 1
< 0.1%
2974262 1
< 0.1%
ValueCountFrequency (%)
2974262 1
< 0.1%
2906907 1
< 0.1%
2463461 1
< 0.1%
2387143 1
< 0.1%

originalNameUsage
Real number (ℝ)

Missing 

Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2682943.25
Minimum2387143
Maximum2974262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:12.148647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2387143
5-th percentile2398590.7
Q12444381.5
median2685184
Q32923745.75
95-th percentile2964158.75
Maximum2974262
Range587119
Interquartile range (IQR)479364.25

Descriptive statistics

Standard deviation300386.7626
Coefficient of variation (CV)0.1119616535
Kurtosis-5.436808163
Mean2682943.25
Median Absolute Deviation (MAD)255400.5
Skewness-0.01224056835
Sum10731773
Variance9.023220713 × 1010
MonotonicityNot monotonic
2025-01-07T12:10:12.198009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2387143 1
 
< 0.1%
2906907 1
 
< 0.1%
2463461 1
 
< 0.1%
2974262 1
 
< 0.1%
(Missing) 338090
> 99.9%
ValueCountFrequency (%)
2387143 1
< 0.1%
2463461 1
< 0.1%
2906907 1
< 0.1%
2974262 1
< 0.1%
ValueCountFrequency (%)
2974262 1
< 0.1%
2906907 1
< 0.1%
2463461 1
< 0.1%
2387143 1
< 0.1%

nameAccordingTo
Real number (ℝ)

Missing 

Distinct2
Distinct (%)50.0%
Missing338090
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3.5
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:12.252389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3.5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.886751346
Coefficient of variation (CV)0.8247860988
Kurtosis-6
Mean3.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum14
Variance8.333333333
MonotonicityNot monotonic
2025-01-07T12:10:12.319267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 2
 
< 0.1%
6 2
 
< 0.1%
(Missing) 338090
> 99.9%
ValueCountFrequency (%)
1 2
< 0.1%
6 2
< 0.1%
ValueCountFrequency (%)
6 2
< 0.1%
1 2
< 0.1%

namePublishedIn
Real number (ℝ)

Missing 

Distinct2
Distinct (%)50.0%
Missing338090
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3853886
Minimum44
Maximum7707728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:12.372426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile44
Q144
median3853886
Q37707728
95-th percentile7707728
Maximum7707728
Range7707684
Interquartile range (IQR)7707684

Descriptive statistics

Standard deviation4450033.432
Coefficient of variation (CV)1.154687355
Kurtosis-6
Mean3853886
Median Absolute Deviation (MAD)3853842
Skewness0
Sum15415544
Variance1.980279755 × 1013
MonotonicityNot monotonic
2025-01-07T12:10:12.435136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
44 2
 
< 0.1%
7707728 2
 
< 0.1%
(Missing) 338090
> 99.9%
ValueCountFrequency (%)
44 2
< 0.1%
7707728 2
< 0.1%
ValueCountFrequency (%)
7707728 2
< 0.1%
44 2
< 0.1%

namePublishedInYear
Real number (ℝ)

Missing 

Distinct2
Distinct (%)66.7%
Missing338091
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3864231
Minimum220
Maximum11592253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:12.494738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile220
Q1220
median220
Q35796236.5
95-th percentile10433049.7
Maximum11592253
Range11592033
Interquartile range (IQR)5796016.5

Descriptive statistics

Standard deviation6692663.373
Coefficient of variation (CV)1.731952198
Kurtosisnan
Mean3864231
Median Absolute Deviation (MAD)0
Skewness1.732050808
Sum11592693
Variance4.479174302 × 1013
MonotonicityNot monotonic
2025-01-07T12:10:12.552603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
220 2
 
< 0.1%
11592253 1
 
< 0.1%
(Missing) 338091
> 99.9%
ValueCountFrequency (%)
220 2
< 0.1%
11592253 1
< 0.1%
ValueCountFrequency (%)
11592253 1
< 0.1%
220 2
< 0.1%

higherClassification
Text

Missing 

Distinct4818
Distinct (%)1.5%
Missing5891
Missing (%)1.7%
Memory size2.6 MiB
2025-01-07T12:10:12.754624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length162
Median length142
Mean length76.55919724
Min length3

Characters and Unicode

Total characters25433195
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique462 ?
Unique (%)0.1%

Sample

1st rowAnimalia, Arthropoda, Insecta, Lepidoptera, Depressariidae, Stenomatinae
2nd rowAnimalia, Annelida, Polychaeta, Sedentaria, Canalipalpata, Sabellida, Siboglinidae
3rd rowAnimalia, Annelida, Polychaeta, Errantia, Amphinomida, Amphinomidae
4th rowAnimalia, Arthropoda, Crustacea, Malacostraca, Eumalacostraca, Eucarida, Decapoda, Pleocyemata, Cambaridae
5th rowPlantae, Pteridophyte, Polypodiales, Dryopteridaceae
ValueCountFrequency (%)
animalia 287414
 
13.0%
arthropoda 145732
 
6.6%
insecta 113112
 
5.1%
chordata 103438
 
4.7%
vertebrata 102398
 
4.6%
lepidoptera 79682
 
3.6%
actinopterygii 40707
 
1.8%
osteichthyes 40705
 
1.8%
neopterygii 40702
 
1.8%
plantae 35513
 
1.6%
Other values (5331) 1219943
55.2%
2025-01-07T12:10:13.096770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3309102
13.0%
i 2169092
 
8.5%
e 2151600
 
8.5%
1877143
 
7.4%
, 1874867
 
7.4%
t 1538441
 
6.0%
r 1525223
 
6.0%
o 1481338
 
5.8%
n 1000711
 
3.9%
d 933466
 
3.7%
Other values (63) 7572212
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25433195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3309102
13.0%
i 2169092
 
8.5%
e 2151600
 
8.5%
1877143
 
7.4%
, 1874867
 
7.4%
t 1538441
 
6.0%
r 1525223
 
6.0%
o 1481338
 
5.8%
n 1000711
 
3.9%
d 933466
 
3.7%
Other values (63) 7572212
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25433195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3309102
13.0%
i 2169092
 
8.5%
e 2151600
 
8.5%
1877143
 
7.4%
, 1874867
 
7.4%
t 1538441
 
6.0%
r 1525223
 
6.0%
o 1481338
 
5.8%
n 1000711
 
3.9%
d 933466
 
3.7%
Other values (63) 7572212
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25433195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3309102
13.0%
i 2169092
 
8.5%
e 2151600
 
8.5%
1877143
 
7.4%
, 1874867
 
7.4%
t 1538441
 
6.0%
r 1525223
 
6.0%
o 1481338
 
5.8%
n 1000711
 
3.9%
d 933466
 
3.7%
Other values (63) 7572212
29.8%
Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:13.164631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.009370203
Min length4

Characters and Unicode

Total characters2707912
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowPlantae
ValueCountFrequency (%)
animalia 291926
84.8%
plantae 35530
 
10.3%
incertae 6107
 
1.8%
sedis 6107
 
1.8%
chromista 3038
 
0.9%
bacteria 1166
 
0.3%
fungi 322
 
0.1%
8518 1
 
< 0.1%
8798 1
 
< 0.1%
9115 1
 
< 0.1%
2025-01-07T12:10:13.289660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 666389
24.6%
i 600592
22.2%
n 333885
12.3%
l 327456
12.1%
m 294964
10.9%
A 291926
10.8%
e 55017
 
2.0%
t 45841
 
1.7%
P 35530
 
1.3%
s 15252
 
0.6%
Other values (18) 41060
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2707912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 666389
24.6%
i 600592
22.2%
n 333885
12.3%
l 327456
12.1%
m 294964
10.9%
A 291926
10.8%
e 55017
 
2.0%
t 45841
 
1.7%
P 35530
 
1.3%
s 15252
 
0.6%
Other values (18) 41060
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2707912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 666389
24.6%
i 600592
22.2%
n 333885
12.3%
l 327456
12.1%
m 294964
10.9%
A 291926
10.8%
e 55017
 
2.0%
t 45841
 
1.7%
P 35530
 
1.3%
s 15252
 
0.6%
Other values (18) 41060
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2707912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 666389
24.6%
i 600592
22.2%
n 333885
12.3%
l 327456
12.1%
m 294964
10.9%
A 291926
10.8%
e 55017
 
2.0%
t 45841
 
1.7%
P 35530
 
1.3%
s 15252
 
0.6%
Other values (18) 41060
 
1.5%

phylum
Text

Missing 

Distinct44
Distinct (%)< 0.1%
Missing6808
Missing (%)2.0%
Memory size2.6 MiB
2025-01-07T12:10:13.356739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.353480075
Min length7

Characters and Unicode

Total characters3098677
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowArthropoda
2nd rowAnnelida
3rd rowAnnelida
4th rowArthropoda
5th rowTracheophyta
ValueCountFrequency (%)
arthropoda 145971
44.1%
chordata 103372
31.2%
tracheophyta 30584
 
9.2%
mollusca 20737
 
6.3%
annelida 11327
 
3.4%
cnidaria 3177
 
1.0%
rhodophyta 2942
 
0.9%
myzozoa 2110
 
0.6%
echinodermata 1630
 
0.5%
chlorophyta 1622
 
0.5%
Other values (34) 7814
 
2.4%
2025-01-07T12:10:13.496087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 473882
15.3%
o 469081
15.1%
r 439672
14.2%
h 325528
10.5%
t 292348
9.4%
d 269484
8.7%
p 182549
 
5.9%
A 157354
 
5.1%
C 109510
 
3.5%
l 56397
 
1.8%
Other values (35) 322872
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3098677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 473882
15.3%
o 469081
15.1%
r 439672
14.2%
h 325528
10.5%
t 292348
9.4%
d 269484
8.7%
p 182549
 
5.9%
A 157354
 
5.1%
C 109510
 
3.5%
l 56397
 
1.8%
Other values (35) 322872
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3098677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 473882
15.3%
o 469081
15.1%
r 439672
14.2%
h 325528
10.5%
t 292348
9.4%
d 269484
8.7%
p 182549
 
5.9%
A 157354
 
5.1%
C 109510
 
3.5%
l 56397
 
1.8%
Other values (35) 322872
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3098677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 473882
15.3%
o 469081
15.1%
r 439672
14.2%
h 325528
10.5%
t 292348
9.4%
d 269484
8.7%
p 182549
 
5.9%
A 157354
 
5.1%
C 109510
 
3.5%
l 56397
 
1.8%
Other values (35) 322872
10.4%

class
Text

Missing 

Distinct105
Distinct (%)< 0.1%
Missing52277
Missing (%)15.5%
Memory size2.6 MiB
2025-01-07T12:10:13.601800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length18
Mean length8.708561772
Min length4

Characters and Unicode

Total characters2489055
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowInsecta
2nd rowPolychaeta
3rd rowPolychaeta
4th rowMalacostraca
5th rowPolypodiopsida
ValueCountFrequency (%)
insecta 112951
39.5%
malacostraca 27895
 
9.8%
mammalia 24478
 
8.6%
amphibia 18384
 
6.4%
magnoliopsida 15795
 
5.5%
liliopsida 10876
 
3.8%
polychaeta 10686
 
3.7%
bivalvia 9771
 
3.4%
gastropoda 9525
 
3.3%
squamata 9481
 
3.3%
Other values (95) 35975
 
12.6%
2025-01-07T12:10:13.792655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 486650
19.6%
s 193470
 
7.8%
c 193177
 
7.8%
t 177830
 
7.1%
i 170083
 
6.8%
e 160201
 
6.4%
o 142892
 
5.7%
n 138528
 
5.6%
l 116613
 
4.7%
I 112951
 
4.5%
Other values (34) 596660
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2489055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 486650
19.6%
s 193470
 
7.8%
c 193177
 
7.8%
t 177830
 
7.1%
i 170083
 
6.8%
e 160201
 
6.4%
o 142892
 
5.7%
n 138528
 
5.6%
l 116613
 
4.7%
I 112951
 
4.5%
Other values (34) 596660
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2489055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 486650
19.6%
s 193470
 
7.8%
c 193177
 
7.8%
t 177830
 
7.1%
i 170083
 
6.8%
e 160201
 
6.4%
o 142892
 
5.7%
n 138528
 
5.6%
l 116613
 
4.7%
I 112951
 
4.5%
Other values (34) 596660
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2489055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 486650
19.6%
s 193470
 
7.8%
c 193177
 
7.8%
t 177830
 
7.1%
i 170083
 
6.8%
e 160201
 
6.4%
o 142892
 
5.7%
n 138528
 
5.6%
l 116613
 
4.7%
I 112951
 
4.5%
Other values (34) 596660
24.0%

order
Text

Missing 

Distinct534
Distinct (%)0.2%
Missing30344
Missing (%)9.0%
Memory size2.6 MiB
2025-01-07T12:10:13.995802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length19
Mean length9.980555646
Min length5

Characters and Unicode

Total characters3071516
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)< 0.1%

Sample

1st rowLepidoptera
2nd rowSabellida
3rd rowAmphinomida
4th rowDecapoda
5th rowPolypodiales
ValueCountFrequency (%)
lepidoptera 79519
25.8%
perciformes 25783
 
8.4%
decapoda 23755
 
7.7%
coleoptera 10132
 
3.3%
anura 10009
 
3.3%
hymenoptera 8496
 
2.8%
rodentia 8406
 
2.7%
caudata 8204
 
2.7%
poales 7858
 
2.6%
cetacea 7808
 
2.5%
Other values (524) 117780
38.3%
2025-01-07T12:10:14.277197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 434314
14.1%
a 364271
11.9%
o 268279
 
8.7%
r 262893
 
8.6%
p 253174
 
8.2%
i 238211
 
7.8%
t 176177
 
5.7%
d 167228
 
5.4%
s 113822
 
3.7%
l 95900
 
3.1%
Other values (49) 697247
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3071516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 434314
14.1%
a 364271
11.9%
o 268279
 
8.7%
r 262893
 
8.6%
p 253174
 
8.2%
i 238211
 
7.8%
t 176177
 
5.7%
d 167228
 
5.4%
s 113822
 
3.7%
l 95900
 
3.1%
Other values (49) 697247
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3071516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 434314
14.1%
a 364271
11.9%
o 268279
 
8.7%
r 262893
 
8.6%
p 253174
 
8.2%
i 238211
 
7.8%
t 176177
 
5.7%
d 167228
 
5.4%
s 113822
 
3.7%
l 95900
 
3.1%
Other values (49) 697247
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3071516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 434314
14.1%
a 364271
11.9%
o 268279
 
8.7%
r 262893
 
8.6%
p 253174
 
8.2%
i 238211
 
7.8%
t 176177
 
5.7%
d 167228
 
5.4%
s 113822
 
3.7%
l 95900
 
3.1%
Other values (49) 697247
22.7%

superfamily
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing338091
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:14.350780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length21
Mean length21
Min length19

Characters and Unicode

Total characters63
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowChampsodon nudivittis
2nd rowCoccocypselum guianense
3rd rowEmoia caeruleocauda
ValueCountFrequency (%)
champsodon 1
16.7%
nudivittis 1
16.7%
coccocypselum 1
16.7%
guianense 1
16.7%
emoia 1
16.7%
caeruleocauda 1
16.7%
2025-01-07T12:10:14.482148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6
 
9.5%
o 6
 
9.5%
c 5
 
7.9%
u 5
 
7.9%
i 5
 
7.9%
e 5
 
7.9%
s 4
 
6.3%
n 4
 
6.3%
m 3
 
4.8%
3
 
4.8%
Other values (11) 17
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6
 
9.5%
o 6
 
9.5%
c 5
 
7.9%
u 5
 
7.9%
i 5
 
7.9%
e 5
 
7.9%
s 4
 
6.3%
n 4
 
6.3%
m 3
 
4.8%
3
 
4.8%
Other values (11) 17
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6
 
9.5%
o 6
 
9.5%
c 5
 
7.9%
u 5
 
7.9%
i 5
 
7.9%
e 5
 
7.9%
s 4
 
6.3%
n 4
 
6.3%
m 3
 
4.8%
3
 
4.8%
Other values (11) 17
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6
 
9.5%
o 6
 
9.5%
c 5
 
7.9%
u 5
 
7.9%
i 5
 
7.9%
e 5
 
7.9%
s 4
 
6.3%
n 4
 
6.3%
m 3
 
4.8%
3
 
4.8%
Other values (11) 17
27.0%

family
Text

Missing 

Distinct3097
Distinct (%)1.0%
Missing19906
Missing (%)5.9%
Memory size2.6 MiB
2025-01-07T12:10:14.677159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length20
Mean length10.83802029
Min length6

Characters and Unicode

Total characters3448528
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)0.1%

Sample

1st rowDepressariidae
2nd rowSiboglinidae
3rd rowAmphinomidae
4th rowCambaridae
5th rowDryopteridaceae
ValueCountFrequency (%)
cambaridae 12102
 
3.8%
geometridae 12012
 
3.8%
noctuidae 7500
 
2.4%
tortricidae 7246
 
2.3%
plethodontidae 6784
 
2.1%
poaceae 6677
 
2.1%
delphinidae 5540
 
1.7%
erebidae 5452
 
1.7%
siboglinidae 5009
 
1.6%
vesicomyidae 4930
 
1.5%
Other values (3098) 244947
77.0%
2025-01-07T12:10:14.945632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 525485
15.2%
a 509708
14.8%
i 440350
12.8%
d 317748
9.2%
r 191641
 
5.6%
o 190413
 
5.5%
c 139936
 
4.1%
t 125374
 
3.6%
l 122376
 
3.5%
n 101480
 
2.9%
Other values (52) 784017
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3448528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 525485
15.2%
a 509708
14.8%
i 440350
12.8%
d 317748
9.2%
r 191641
 
5.6%
o 190413
 
5.5%
c 139936
 
4.1%
t 125374
 
3.6%
l 122376
 
3.5%
n 101480
 
2.9%
Other values (52) 784017
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3448528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 525485
15.2%
a 509708
14.8%
i 440350
12.8%
d 317748
9.2%
r 191641
 
5.6%
o 190413
 
5.5%
c 139936
 
4.1%
t 125374
 
3.6%
l 122376
 
3.5%
n 101480
 
2.9%
Other values (52) 784017
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3448528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 525485
15.2%
a 509708
14.8%
i 440350
12.8%
d 317748
9.2%
r 191641
 
5.6%
o 190413
 
5.5%
c 139936
 
4.1%
t 125374
 
3.6%
l 122376
 
3.5%
n 101480
 
2.9%
Other values (52) 784017
22.7%

subfamily
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:15.023043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length20
Mean length19.75
Min length16

Characters and Unicode

Total characters79
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowChampsodon nudivittis
2nd rowCoccocypselum guianense
3rd rowEmoia caeruleocauda
4th rowDimorphandra sp.
ValueCountFrequency (%)
champsodon 1
12.5%
nudivittis 1
12.5%
coccocypselum 1
12.5%
guianense 1
12.5%
emoia 1
12.5%
caeruleocauda 1
12.5%
dimorphandra 1
12.5%
sp 1
12.5%
2025-01-07T12:10:15.156247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8
 
10.1%
o 7
 
8.9%
i 6
 
7.6%
u 5
 
6.3%
c 5
 
6.3%
n 5
 
6.3%
s 5
 
6.3%
e 5
 
6.3%
4
 
5.1%
m 4
 
5.1%
Other values (13) 25
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8
 
10.1%
o 7
 
8.9%
i 6
 
7.6%
u 5
 
6.3%
c 5
 
6.3%
n 5
 
6.3%
s 5
 
6.3%
e 5
 
6.3%
4
 
5.1%
m 4
 
5.1%
Other values (13) 25
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8
 
10.1%
o 7
 
8.9%
i 6
 
7.6%
u 5
 
6.3%
c 5
 
6.3%
n 5
 
6.3%
s 5
 
6.3%
e 5
 
6.3%
4
 
5.1%
m 4
 
5.1%
Other values (13) 25
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8
 
10.1%
o 7
 
8.9%
i 6
 
7.6%
u 5
 
6.3%
c 5
 
6.3%
n 5
 
6.3%
s 5
 
6.3%
e 5
 
6.3%
4
 
5.1%
m 4
 
5.1%
Other values (13) 25
31.6%

tribe
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

subtribe
Text

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:15.204695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
ValueCountFrequency (%)
eml 4
100.0%
2025-01-07T12:10:15.380844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4
33.3%
M 4
33.3%
L 4
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4
33.3%
M 4
33.3%
L 4
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4
33.3%
M 4
33.3%
L 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4
33.3%
M 4
33.3%
L 4
33.3%

genus
Text

Missing 

Distinct19311
Distinct (%)6.4%
Missing34392
Missing (%)10.2%
Memory size2.6 MiB
2025-01-07T12:10:15.550841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length9.330027461
Min length3

Characters and Unicode

Total characters2833548
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2143 ?
Unique (%)0.7%

Sample

1st rowRectiostoma
2nd rowPolystichum
3rd rowMesontoplatys
4th rowDulcerana
5th rowAmanses
ValueCountFrequency (%)
plethodon 4671
 
1.5%
faxonius 4236
 
1.4%
procambarus 3675
 
1.2%
bathymodiolus 2587
 
0.9%
riftia 2006
 
0.7%
tursiops 1919
 
0.6%
cambarus 1707
 
0.6%
delphinus 1662
 
0.5%
aegla 1424
 
0.5%
anolis 1420
 
0.5%
Other values (19301) 278395
91.7%
2025-01-07T12:10:15.798341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 312625
 
11.0%
o 247355
 
8.7%
i 219282
 
7.7%
s 204987
 
7.2%
e 199721
 
7.0%
r 183312
 
6.5%
t 142209
 
5.0%
l 138064
 
4.9%
n 131110
 
4.6%
u 127288
 
4.5%
Other values (55) 927595
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2833548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 312625
 
11.0%
o 247355
 
8.7%
i 219282
 
7.7%
s 204987
 
7.2%
e 199721
 
7.0%
r 183312
 
6.5%
t 142209
 
5.0%
l 138064
 
4.9%
n 131110
 
4.6%
u 127288
 
4.5%
Other values (55) 927595
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2833548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 312625
 
11.0%
o 247355
 
8.7%
i 219282
 
7.7%
s 204987
 
7.2%
e 199721
 
7.0%
r 183312
 
6.5%
t 142209
 
5.0%
l 138064
 
4.9%
n 131110
 
4.6%
u 127288
 
4.5%
Other values (55) 927595
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2833548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 312625
 
11.0%
o 247355
 
8.7%
i 219282
 
7.7%
s 204987
 
7.2%
e 199721
 
7.0%
r 183312
 
6.5%
t 142209
 
5.0%
l 138064
 
4.9%
n 131110
 
4.6%
u 127288
 
4.5%
Other values (55) 927595
32.7%

genericName
Text

Missing 

Distinct19280
Distinct (%)6.3%
Missing34393
Missing (%)10.2%
Memory size2.6 MiB
2025-01-07T12:10:16.013698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length9.355540482
Min length3

Characters and Unicode

Total characters2841287
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2065 ?
Unique (%)0.7%

Sample

1st rowRectiostoma
2nd rowPolystichum
3rd rowMesontoplatys
4th rowBursa
5th rowAmanses
ValueCountFrequency (%)
plethodon 4671
 
1.5%
orconectes 4548
 
1.5%
procambarus 3716
 
1.2%
bathymodiolus 2598
 
0.9%
riftia 2006
 
0.7%
tursiops 1919
 
0.6%
cambarus 1853
 
0.6%
delphinus 1662
 
0.5%
aegla 1424
 
0.5%
anolis 1388
 
0.5%
Other values (19270) 277916
91.5%
2025-01-07T12:10:16.300435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 309944
 
10.9%
o 245779
 
8.7%
i 214451
 
7.5%
e 208150
 
7.3%
s 204498
 
7.2%
r 186229
 
6.6%
t 147357
 
5.2%
l 138695
 
4.9%
n 130165
 
4.6%
u 122278
 
4.3%
Other values (50) 933741
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2841287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 309944
 
10.9%
o 245779
 
8.7%
i 214451
 
7.5%
e 208150
 
7.3%
s 204498
 
7.2%
r 186229
 
6.6%
t 147357
 
5.2%
l 138695
 
4.9%
n 130165
 
4.6%
u 122278
 
4.3%
Other values (50) 933741
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2841287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 309944
 
10.9%
o 245779
 
8.7%
i 214451
 
7.5%
e 208150
 
7.3%
s 204498
 
7.2%
r 186229
 
6.6%
t 147357
 
5.2%
l 138695
 
4.9%
n 130165
 
4.6%
u 122278
 
4.3%
Other values (50) 933741
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2841287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 309944
 
10.9%
o 245779
 
8.7%
i 214451
 
7.5%
e 208150
 
7.3%
s 204498
 
7.2%
r 186229
 
6.6%
t 147357
 
5.2%
l 138695
 
4.9%
n 130165
 
4.6%
u 122278
 
4.3%
Other values (50) 933741
32.9%

subgenus
Boolean

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
True
 
4
(Missing)
338090 
ValueCountFrequency (%)
True 4
 
< 0.1%
(Missing) 338090
> 99.9%
2025-01-07T12:10:16.370080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

infragenericEpithet
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

specificEpithet
Text

Missing 

Distinct22410
Distinct (%)9.0%
Missing89523
Missing (%)26.5%
Memory size2.6 MiB
2025-01-07T12:10:16.528539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length8.891676825
Min length2

Characters and Unicode

Total characters2210213
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3280 ?
Unique (%)1.3%

Sample

1st rowfernaldella
2nd rowbolzi
3rd rowgranularis
4th rowscopas
5th rowextenta
ValueCountFrequency (%)
truncatus 1929
 
0.8%
cinereus 1842
 
0.7%
delphis 1660
 
0.7%
porphyriticus 815
 
0.3%
acutus 778
 
0.3%
opacum 765
 
0.3%
hoffmani 639
 
0.3%
maculatus 632
 
0.3%
nigripes 624
 
0.3%
carolinensis 597
 
0.2%
Other values (22400) 238290
95.9%
2025-01-07T12:10:16.791552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 289852
13.1%
i 243869
11.0%
s 189586
 
8.6%
e 172279
 
7.8%
r 150335
 
6.8%
l 148609
 
6.7%
u 138502
 
6.3%
n 137838
 
6.2%
t 126139
 
5.7%
o 112282
 
5.1%
Other values (18) 500922
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2210213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 289852
13.1%
i 243869
11.0%
s 189586
 
8.6%
e 172279
 
7.8%
r 150335
 
6.8%
l 148609
 
6.7%
u 138502
 
6.3%
n 137838
 
6.2%
t 126139
 
5.7%
o 112282
 
5.1%
Other values (18) 500922
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2210213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 289852
13.1%
i 243869
11.0%
s 189586
 
8.6%
e 172279
 
7.8%
r 150335
 
6.8%
l 148609
 
6.7%
u 138502
 
6.3%
n 137838
 
6.2%
t 126139
 
5.7%
o 112282
 
5.1%
Other values (18) 500922
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2210213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 289852
13.1%
i 243869
11.0%
s 189586
 
8.6%
e 172279
 
7.8%
r 150335
 
6.8%
l 148609
 
6.7%
u 138502
 
6.3%
n 137838
 
6.2%
t 126139
 
5.7%
o 112282
 
5.1%
Other values (18) 500922
22.7%

infraspecificEpithet
Text

Missing 

Distinct1598
Distinct (%)17.6%
Missing328999
Missing (%)97.3%
Memory size2.6 MiB
2025-01-07T12:10:17.004793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.015063222
Min length3

Characters and Unicode

Total characters81992
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique647 ?
Unique (%)7.1%

Sample

1st rowcinereus
2nd rowbenjamina
3rd rowmexicana
4th rowdoliatus
5th rowpallidirostris
ValueCountFrequency (%)
pennsylvanicus 615
 
6.8%
cinereus 494
 
5.4%
talpoides 246
 
2.7%
melas 245
 
2.7%
dickeyi 167
 
1.8%
meeki 106
 
1.2%
porteri 91
 
1.0%
fumeus 88
 
1.0%
parva 74
 
0.8%
couguar 61
 
0.7%
Other values (1588) 6908
76.0%
2025-01-07T12:10:17.285059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9129
11.1%
a 8963
10.9%
s 8483
10.3%
e 7210
 
8.8%
n 6911
 
8.4%
u 5147
 
6.3%
r 5031
 
6.1%
l 4672
 
5.7%
c 4450
 
5.4%
o 3740
 
4.6%
Other values (17) 18256
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 9129
11.1%
a 8963
10.9%
s 8483
10.3%
e 7210
 
8.8%
n 6911
 
8.4%
u 5147
 
6.3%
r 5031
 
6.1%
l 4672
 
5.7%
c 4450
 
5.4%
o 3740
 
4.6%
Other values (17) 18256
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 9129
11.1%
a 8963
10.9%
s 8483
10.3%
e 7210
 
8.8%
n 6911
 
8.4%
u 5147
 
6.3%
r 5031
 
6.1%
l 4672
 
5.7%
c 4450
 
5.4%
o 3740
 
4.6%
Other values (17) 18256
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 9129
11.1%
a 8963
10.9%
s 8483
10.3%
e 7210
 
8.8%
n 6911
 
8.4%
u 5147
 
6.3%
r 5031
 
6.1%
l 4672
 
5.7%
c 4450
 
5.4%
o 3740
 
4.6%
Other values (17) 18256
22.3%

cultivarEpithet
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:17.350605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length9.25
Min length4

Characters and Unicode

Total characters37
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowASIA
2nd rowLATIN_AMERICA
3rd rowOCEANIA
4th rowLATIN_AMERICA
ValueCountFrequency (%)
latin_america 2
50.0%
asia 1
25.0%
oceania 1
25.0%
2025-01-07T12:10:17.463470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10
27.0%
I 6
16.2%
E 3
 
8.1%
N 3
 
8.1%
C 3
 
8.1%
L 2
 
5.4%
T 2
 
5.4%
_ 2
 
5.4%
M 2
 
5.4%
R 2
 
5.4%
Other values (2) 2
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10
27.0%
I 6
16.2%
E 3
 
8.1%
N 3
 
8.1%
C 3
 
8.1%
L 2
 
5.4%
T 2
 
5.4%
_ 2
 
5.4%
M 2
 
5.4%
R 2
 
5.4%
Other values (2) 2
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10
27.0%
I 6
16.2%
E 3
 
8.1%
N 3
 
8.1%
C 3
 
8.1%
L 2
 
5.4%
T 2
 
5.4%
_ 2
 
5.4%
M 2
 
5.4%
R 2
 
5.4%
Other values (2) 2
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10
27.0%
I 6
16.2%
E 3
 
8.1%
N 3
 
8.1%
C 3
 
8.1%
L 2
 
5.4%
T 2
 
5.4%
_ 2
 
5.4%
M 2
 
5.4%
R 2
 
5.4%
Other values (2) 2
 
5.4%
Distinct11
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:17.528924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length7
Mean length6.638623101
Min length4

Characters and Unicode

Total characters2244472
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPECIES
2nd rowFAMILY
3rd rowFAMILY
4th rowFAMILY
5th rowGENUS
ValueCountFrequency (%)
species 239484
70.8%
genus 55123
 
16.3%
family 14870
 
4.4%
subspecies 8236
 
2.4%
kingdom 6690
 
2.0%
order 4994
 
1.5%
phylum 4014
 
1.2%
class 3823
 
1.1%
variety 806
 
0.2%
form 49
 
< 0.1%
2025-01-07T12:10:17.656102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 566445
25.2%
E 556367
24.8%
I 270090
12.0%
P 251734
11.2%
C 251547
11.2%
U 67373
 
3.0%
N 61817
 
2.8%
G 61813
 
2.8%
M 25627
 
1.1%
L 22707
 
1.0%
Other values (12) 108952
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2244472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 566445
25.2%
E 556367
24.8%
I 270090
12.0%
P 251734
11.2%
C 251547
11.2%
U 67373
 
3.0%
N 61817
 
2.8%
G 61813
 
2.8%
M 25627
 
1.1%
L 22707
 
1.0%
Other values (12) 108952
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2244472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 566445
25.2%
E 556367
24.8%
I 270090
12.0%
P 251734
11.2%
C 251547
11.2%
U 67373
 
3.0%
N 61817
 
2.8%
G 61813
 
2.8%
M 25627
 
1.1%
L 22707
 
1.0%
Other values (12) 108952
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2244472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 566445
25.2%
E 556367
24.8%
I 270090
12.0%
P 251734
11.2%
C 251547
11.2%
U 67373
 
3.0%
N 61817
 
2.8%
G 61813
 
2.8%
M 25627
 
1.1%
L 22707
 
1.0%
Other values (12) 108952
 
4.9%

verbatimTaxonRank
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:17.707612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowPHL
2nd rowGUY
3rd rowPLW
4th rowGUY
ValueCountFrequency (%)
guy 2
50.0%
phl 1
25.0%
plw 1
25.0%
2025-01-07T12:10:17.809082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 2
16.7%
U 2
16.7%
Y 2
16.7%
P 2
16.7%
L 2
16.7%
H 1
8.3%
W 1
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 2
16.7%
U 2
16.7%
Y 2
16.7%
P 2
16.7%
L 2
16.7%
H 1
8.3%
W 1
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 2
16.7%
U 2
16.7%
Y 2
16.7%
P 2
16.7%
L 2
16.7%
H 1
8.3%
W 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 2
16.7%
U 2
16.7%
Y 2
16.7%
P 2
16.7%
L 2
16.7%
H 1
8.3%
W 1
8.3%

vernacularName
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:17.856458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length8.5
Mean length7
Min length5

Characters and Unicode

Total characters28
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowPhilippines
2nd rowGuyana
3rd rowPalau
4th rowGuyana
ValueCountFrequency (%)
guyana 2
50.0%
philippines 1
25.0%
palau 1
25.0%
2025-01-07T12:10:17.969558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6
21.4%
u 3
10.7%
i 3
10.7%
n 3
10.7%
y 2
 
7.1%
G 2
 
7.1%
P 2
 
7.1%
l 2
 
7.1%
p 2
 
7.1%
h 1
 
3.6%
Other values (2) 2
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6
21.4%
u 3
10.7%
i 3
10.7%
n 3
10.7%
y 2
 
7.1%
G 2
 
7.1%
P 2
 
7.1%
l 2
 
7.1%
p 2
 
7.1%
h 1
 
3.6%
Other values (2) 2
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6
21.4%
u 3
10.7%
i 3
10.7%
n 3
10.7%
y 2
 
7.1%
G 2
 
7.1%
P 2
 
7.1%
l 2
 
7.1%
p 2
 
7.1%
h 1
 
3.6%
Other values (2) 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6
21.4%
u 3
10.7%
i 3
10.7%
n 3
10.7%
y 2
 
7.1%
G 2
 
7.1%
P 2
 
7.1%
l 2
 
7.1%
p 2
 
7.1%
h 1
 
3.6%
Other values (2) 2
 
7.1%

nomenclaturalCode
Text

Missing 

Distinct3
Distinct (%)75.0%
Missing338090
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:18.024396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.25
Min length7

Characters and Unicode

Total characters29
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowPHL.36_1
2nd rowGUY.2_1
3rd rowPLW.6_1
4th rowGUY.2_1
ValueCountFrequency (%)
guy.2_1 2
50.0%
phl.36_1 1
25.0%
plw.6_1 1
25.0%
2025-01-07T12:10:18.134931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4
13.8%
1 4
13.8%
_ 4
13.8%
G 2
6.9%
U 2
6.9%
2 2
6.9%
Y 2
6.9%
P 2
6.9%
L 2
6.9%
6 2
6.9%
Other values (3) 3
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4
13.8%
1 4
13.8%
_ 4
13.8%
G 2
6.9%
U 2
6.9%
2 2
6.9%
Y 2
6.9%
P 2
6.9%
L 2
6.9%
6 2
6.9%
Other values (3) 3
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4
13.8%
1 4
13.8%
_ 4
13.8%
G 2
6.9%
U 2
6.9%
2 2
6.9%
Y 2
6.9%
P 2
6.9%
L 2
6.9%
6 2
6.9%
Other values (3) 3
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4
13.8%
1 4
13.8%
_ 4
13.8%
G 2
6.9%
U 2
6.9%
2 2
6.9%
Y 2
6.9%
P 2
6.9%
L 2
6.9%
6 2
6.9%
Other values (3) 3
10.3%

taxonomicStatus
Text

Missing 

Distinct6
Distinct (%)< 0.1%
Missing6108
Missing (%)1.8%
Memory size2.6 MiB
2025-01-07T12:10:18.188335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length8
Mean length7.926999934
Min length5

Characters and Unicode

Total characters2631653
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowACCEPTED
2nd rowACCEPTED
3rd rowACCEPTED
4th rowACCEPTED
5th rowACCEPTED
ValueCountFrequency (%)
accepted 305150
91.9%
synonym 24244
 
7.3%
doubtful 2588
 
0.8%
cuyuni-mazaruni 2
 
< 0.1%
iloilo 1
 
< 0.1%
koror 1
 
< 0.1%
2025-01-07T12:10:18.302535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 610302
23.2%
E 610300
23.2%
T 307738
11.7%
D 307738
11.7%
A 305150
11.6%
P 305150
11.6%
Y 48488
 
1.8%
N 48488
 
1.8%
O 26832
 
1.0%
M 24246
 
0.9%
Other values (17) 37221
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2631653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 610302
23.2%
E 610300
23.2%
T 307738
11.7%
D 307738
11.7%
A 305150
11.6%
P 305150
11.6%
Y 48488
 
1.8%
N 48488
 
1.8%
O 26832
 
1.0%
M 24246
 
0.9%
Other values (17) 37221
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2631653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 610302
23.2%
E 610300
23.2%
T 307738
11.7%
D 307738
11.7%
A 305150
11.6%
P 305150
11.6%
Y 48488
 
1.8%
N 48488
 
1.8%
O 26832
 
1.0%
M 24246
 
0.9%
Other values (17) 37221
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2631653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 610302
23.2%
E 610300
23.2%
T 307738
11.7%
D 307738
11.7%
A 305150
11.6%
P 305150
11.6%
Y 48488
 
1.8%
N 48488
 
1.8%
O 26832
 
1.0%
M 24246
 
0.9%
Other values (17) 37221
 
1.4%

nomenclaturalStatus
Text

Missing 

Distinct2
Distinct (%)66.7%
Missing338091
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:18.350598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.666666667
Min length9

Characters and Unicode

Total characters29
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowPHL.36.21_1
2nd rowGUY.2.8_1
3rd rowGUY.2.8_1
ValueCountFrequency (%)
guy.2.8_1 2
66.7%
phl.36.21_1 1
33.3%
2025-01-07T12:10:18.466720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 6
20.7%
1 4
13.8%
2 3
10.3%
_ 3
10.3%
G 2
 
6.9%
U 2
 
6.9%
Y 2
 
6.9%
8 2
 
6.9%
P 1
 
3.4%
H 1
 
3.4%
Other values (3) 3
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 6
20.7%
1 4
13.8%
2 3
10.3%
_ 3
10.3%
G 2
 
6.9%
U 2
 
6.9%
Y 2
 
6.9%
8 2
 
6.9%
P 1
 
3.4%
H 1
 
3.4%
Other values (3) 3
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 6
20.7%
1 4
13.8%
2 3
10.3%
_ 3
10.3%
G 2
 
6.9%
U 2
 
6.9%
Y 2
 
6.9%
8 2
 
6.9%
P 1
 
3.4%
H 1
 
3.4%
Other values (3) 3
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 6
20.7%
1 4
13.8%
2 3
10.3%
_ 3
10.3%
G 2
 
6.9%
U 2
 
6.9%
Y 2
 
6.9%
8 2
 
6.9%
P 1
 
3.4%
H 1
 
3.4%
Other values (3) 3
10.3%

taxonRemarks
Text

Missing 

Distinct2
Distinct (%)66.7%
Missing338091
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:18.518919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length16
Mean length14.33333333
Min length11

Characters and Unicode

Total characters43
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowIloilo City
2nd rowRest of Region 7
3rd rowRest of Region 7
ValueCountFrequency (%)
rest 2
20.0%
of 2
20.0%
region 2
20.0%
7 2
20.0%
iloilo 1
10.0%
city 1
10.0%
2025-01-07T12:10:18.634503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
16.3%
o 6
14.0%
R 4
9.3%
e 4
9.3%
i 4
9.3%
t 3
7.0%
s 2
 
4.7%
f 2
 
4.7%
g 2
 
4.7%
n 2
 
4.7%
Other values (5) 7
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7
16.3%
o 6
14.0%
R 4
9.3%
e 4
9.3%
i 4
9.3%
t 3
7.0%
s 2
 
4.7%
f 2
 
4.7%
g 2
 
4.7%
n 2
 
4.7%
Other values (5) 7
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7
16.3%
o 6
14.0%
R 4
9.3%
e 4
9.3%
i 4
9.3%
t 3
7.0%
s 2
 
4.7%
f 2
 
4.7%
g 2
 
4.7%
n 2
 
4.7%
Other values (5) 7
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7
16.3%
o 6
14.0%
R 4
9.3%
e 4
9.3%
i 4
9.3%
t 3
7.0%
s 2
 
4.7%
f 2
 
4.7%
g 2
 
4.7%
n 2
 
4.7%
Other values (5) 7
16.3%
Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:18.702018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.99993493
Min length14

Characters and Unicode

Total characters12171218
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row26098c25-8f7f-4c71-97ac-1d3db181c65e
2nd row26098c25-8f7f-4c71-97ac-1d3db181c65e
3rd row26098c25-8f7f-4c71-97ac-1d3db181c65e
4th row26098c25-8f7f-4c71-97ac-1d3db181c65e
5th row26098c25-8f7f-4c71-97ac-1d3db181c65e
ValueCountFrequency (%)
26098c25-8f7f-4c71-97ac-1d3db181c65e 338089
> 99.9%
phl.36.21.66_1 1
 
< 0.1%
2025-01-07T12:10:18.823170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1352358
11.1%
c 1352356
11.1%
- 1352356
11.1%
7 1014267
 
8.3%
8 1014267
 
8.3%
6 676181
 
5.6%
2 676179
 
5.6%
9 676178
 
5.6%
f 676178
 
5.6%
5 676178
 
5.6%
Other values (12) 2704720
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12171218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1352358
11.1%
c 1352356
11.1%
- 1352356
11.1%
7 1014267
 
8.3%
8 1014267
 
8.3%
6 676181
 
5.6%
2 676179
 
5.6%
9 676178
 
5.6%
f 676178
 
5.6%
5 676178
 
5.6%
Other values (12) 2704720
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12171218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1352358
11.1%
c 1352356
11.1%
- 1352356
11.1%
7 1014267
 
8.3%
8 1014267
 
8.3%
6 676181
 
5.6%
2 676179
 
5.6%
9 676178
 
5.6%
f 676178
 
5.6%
5 676178
 
5.6%
Other values (12) 2704720
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12171218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1352358
11.1%
c 1352356
11.1%
- 1352356
11.1%
7 1014267
 
8.3%
8 1014267
 
8.3%
6 676181
 
5.6%
2 676179
 
5.6%
9 676178
 
5.6%
f 676178
 
5.6%
5 676178
 
5.6%
Other values (12) 2704720
22.2%
Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:18.871724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.000020705
Min length2

Characters and Unicode

Total characters676187
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 338089
> 99.9%
kahirupan 1
 
< 0.1%
2025-01-07T12:10:18.983795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 338089
50.0%
S 338089
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 676187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 338089
50.0%
S 338089
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 676187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 338089
50.0%
S 338089
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 676187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 338089
50.0%
S 338089
50.0%
a 2
 
< 0.1%
K 1
 
< 0.1%
h 1
 
< 0.1%
i 1
 
< 0.1%
r 1
 
< 0.1%
u 1
 
< 0.1%
p 1
 
< 0.1%
n 1
 
< 0.1%
Distinct31889
Distinct (%)9.4%
Missing1
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:19.098624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.9958118
Min length2

Characters and Unicode

Total characters8112816
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3501 ?
Unique (%)1.0%

Sample

1st row2024-12-01T12:07:01.240Z
2nd row2024-12-01T12:07:01.438Z
3rd row2024-12-01T12:07:01.443Z
4th row2024-12-01T12:07:01.449Z
5th row2024-12-01T12:07:01.465Z
ValueCountFrequency (%)
2024-12-01t12:07:38.532z 73
 
< 0.1%
2024-12-01t12:07:38.533z 71
 
< 0.1%
2024-12-01t12:07:38.508z 68
 
< 0.1%
2024-12-01t12:07:39.879z 67
 
< 0.1%
2024-12-01t12:07:37.936z 65
 
< 0.1%
2024-12-01t12:07:39.819z 65
 
< 0.1%
2024-12-01t12:07:40.339z 65
 
< 0.1%
2024-12-01t12:07:39.875z 64
 
< 0.1%
2024-12-01t12:07:39.723z 64
 
< 0.1%
2024-12-01t12:07:38.854z 63
 
< 0.1%
Other values (31879) 337428
99.8%
2025-01-07T12:10:19.286203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (9) 866878
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8112816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (9) 866878
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8112816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (9) 866878
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8112816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (9) 866878
10.7%

elevation
Real number (ℝ)

Missing 

Distinct2734
Distinct (%)3.1%
Missing248950
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean979.7544429
Minimum-9
Maximum9000
Zeros142
Zeros (%)< 0.1%
Negative12
Negative (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:19.511809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile10
Q1140
median700
Q31554
95-th percentile2900
Maximum9000
Range9009
Interquartile range (IQR)1414

Descriptive statistics

Standard deviation973.5526499
Coefficient of variation (CV)0.9936700537
Kurtosis1.082672038
Mean979.7544429
Median Absolute Deviation (MAD)614
Skewness1.150266411
Sum87339230.06
Variance947804.7622
MonotonicityNot monotonic
2025-01-07T12:10:19.576973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1541
 
0.5%
1100 1195
 
0.4%
1200 995
 
0.3%
150 967
 
0.3%
200 779
 
0.2%
1829 757
 
0.2%
50 700
 
0.2%
300 694
 
0.2%
1487 632
 
0.2%
100 612
 
0.2%
Other values (2724) 80272
 
23.7%
(Missing) 248950
73.6%
ValueCountFrequency (%)
-9 7
 
< 0.1%
-5 4
 
< 0.1%
-3 1
 
< 0.1%
0 142
 
< 0.1%
1 422
0.1%
ValueCountFrequency (%)
9000 1
 
< 0.1%
8700 1
 
< 0.1%
8000 3
< 0.1%
6850 3
< 0.1%
6600 3
< 0.1%

elevationAccuracy
Real number (ℝ)

Missing  Zeros 

Distinct161
Distinct (%)0.3%
Missing284393
Missing (%)84.1%
Infinite0
Infinite (%)0.0%
Mean4.594141636
Minimum0
Maximum1000
Zeros47745
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:19.648324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum1000
Range1000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.2411818
Coefficient of variation (CV)6.14721618
Kurtosis337.8887406
Mean4.594141636
Median Absolute Deviation (MAD)0
Skewness15.25159513
Sum246710
Variance797.5643495
MonotonicityNot monotonic
2025-01-07T12:10:19.723092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47745
 
14.1%
2.5 1013
 
0.3%
25 410
 
0.1%
100 392
 
0.1%
1 356
 
0.1%
5 307
 
0.1%
12.5 218
 
0.1%
50 212
 
0.1%
38.75 206
 
0.1%
30.5 167
 
< 0.1%
Other values (151) 2675
 
0.8%
(Missing) 284393
84.1%
ValueCountFrequency (%)
0 47745
14.1%
0.5 153
 
< 0.1%
1 356
 
0.1%
1.25 21
 
< 0.1%
1.5 25
 
< 0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
762 2
 
< 0.1%
750 23
< 0.1%
716 3
 
< 0.1%
647.25 3
 
< 0.1%

depth
Real number (ℝ)

Missing 

Distinct2252
Distinct (%)3.0%
Missing262666
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean524.6038229
Minimum0
Maximum5981.18
Zeros937
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:19.792735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12
median14.6
Q3548.5
95-th percentile2630
Maximum5981.18
Range5981.18
Interquartile range (IQR)546.5

Descriptive statistics

Standard deviation949.521717
Coefficient of variation (CV)1.809978646
Kurtosis1.766747382
Mean524.6038229
Median Absolute Deviation (MAD)13.85
Skewness1.740133722
Sum39569817.16
Variance901591.4911
MonotonicityNot monotonic
2025-01-07T12:10:19.863304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 5165
 
1.5%
3 4287
 
1.3%
1.5 4042
 
1.2%
1 3416
 
1.0%
2 1940
 
0.6%
10 1543
 
0.5%
15 1120
 
0.3%
0 937
 
0.3%
17.5 933
 
0.3%
2.5 888
 
0.3%
Other values (2242) 51157
 
15.1%
(Missing) 262666
77.7%
ValueCountFrequency (%)
0 937
0.3%
0.065 7
 
< 0.1%
0.1 50
 
< 0.1%
0.15 52
 
< 0.1%
0.2 48
 
< 0.1%
ValueCountFrequency (%)
5981.18 1
 
< 0.1%
5951.05 1
 
< 0.1%
5201 1
 
< 0.1%
4965.57 1
 
< 0.1%
4947 26
< 0.1%

depthAccuracy
Real number (ℝ)

Missing  Zeros 

Distinct238
Distinct (%)0.4%
Missing272182
Missing (%)80.5%
Infinite0
Infinite (%)0.0%
Mean8.88035115
Minimum0
Maximum780.45
Zeros26153
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:19.936993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q33
95-th percentile49
Maximum780.45
Range780.45
Interquartile range (IQR)3

Descriptive statistics

Standard deviation38.28041758
Coefficient of variation (CV)4.310687374
Kurtosis181.8209839
Mean8.88035115
Median Absolute Deviation (MAD)0.5
Skewness11.78039442
Sum585321.705
Variance1465.39037
MonotonicityNot monotonic
2025-01-07T12:10:20.030920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26153
 
7.7%
0.5 5488
 
1.6%
1.5 4808
 
1.4%
1 2977
 
0.9%
2 2346
 
0.7%
2.5 2341
 
0.7%
3 1911
 
0.6%
0.25 1393
 
0.4%
5 1251
 
0.4%
4 1139
 
0.3%
Other values (228) 16105
 
4.8%
(Missing) 272182
80.5%
ValueCountFrequency (%)
0 26153
7.7%
0.035 7
 
< 0.1%
0.05 4
 
< 0.1%
0.1 58
 
< 0.1%
0.1 19
 
< 0.1%
ValueCountFrequency (%)
780.45 1
 
< 0.1%
770.5 2
 
< 0.1%
767.5 39
< 0.1%
668.86 1
 
< 0.1%
650 47
< 0.1%

distanceFromCentroidInMeters
Real number (ℝ)

Missing 

Distinct170
Distinct (%)6.3%
Missing335404
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean2912.887225
Minimum0
Maximum4973.782789
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:20.104876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile818.121102
Q11895.275346
median3247.910832
Q34027.579915
95-th percentile4819.432257
Maximum4973.782789
Range4973.782789
Interquartile range (IQR)2132.304569

Descriptive statistics

Standard deviation1344.934209
Coefficient of variation (CV)0.4617185993
Kurtosis-1.230281346
Mean2912.887225
Median Absolute Deviation (MAD)1024.112431
Skewness-0.2768735347
Sum7835666.635
Variance1808848.028
MonotonicityNot monotonic
2025-01-07T12:10:20.181582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3997.886559 239
 
0.1%
818.121102 183
 
0.1%
918.1358065 159
 
< 0.1%
3435.299369 143
 
< 0.1%
1914.901062 138
 
< 0.1%
4049.579333 132
 
< 0.1%
3247.910832 94
 
< 0.1%
3286.338393 91
 
< 0.1%
2259.882955 89
 
< 0.1%
3868.839759 70
 
< 0.1%
Other values (160) 1352
 
0.4%
(Missing) 335404
99.2%
ValueCountFrequency (%)
0 7
< 0.1%
67.25063149 3
< 0.1%
235.3110738 2
 
< 0.1%
247.4780297 3
< 0.1%
300.8106775 3
< 0.1%
ValueCountFrequency (%)
4973.782789 7
 
< 0.1%
4965.486359 3
 
< 0.1%
4954.93003 35
< 0.1%
4942.882538 4
 
< 0.1%
4933.433353 3
 
< 0.1%

issue
Text

Missing 

Distinct172
Distinct (%)0.1%
Missing45626
Missing (%)13.5%
Memory size2.6 MiB
2025-01-07T12:10:20.248538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length197
Median length154
Mean length59.83685737
Min length15

Characters and Unicode

Total characters17500366
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowGEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES
2nd rowGEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_INVALID
3rd rowGEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES;CONTINENT_INVALID
4th rowGEODETIC_DATUM_ASSUMED_WGS84
5th rowGEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES
ValueCountFrequency (%)
geodetic_datum_assumed_wgs84;continent_derived_from_coordinates 81966
28.0%
geodetic_datum_assumed_wgs84 50425
17.2%
geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 48259
16.5%
geodetic_datum_assumed_wgs84;continent_invalid 23627
 
8.1%
continent_derived_from_country 13936
 
4.8%
geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates;continent_invalid 12417
 
4.2%
geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;taxon_match_higherrank 10213
 
3.5%
continent_derived_from_country;continent_invalid 6285
 
2.1%
geodetic_datum_assumed_wgs84;geodetic_datum_invalid;continent_derived_from_coordinates 4317
 
1.5%
country_derived_from_coordinates;geodetic_datum_assumed_wgs84;continent_invalid 3977
 
1.4%
Other values (162) 37046
12.7%
2025-01-07T12:10:20.399889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1740521
 
9.9%
_ 1614935
 
9.2%
D 1532749
 
8.8%
T 1465021
 
8.4%
N 1338780
 
7.7%
I 1261875
 
7.2%
O 1227170
 
7.0%
S 956544
 
5.5%
A 942202
 
5.4%
C 851797
 
4.9%
Other values (18) 4568772
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17500366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1740521
 
9.9%
_ 1614935
 
9.2%
D 1532749
 
8.8%
T 1465021
 
8.4%
N 1338780
 
7.7%
I 1261875
 
7.2%
O 1227170
 
7.0%
S 956544
 
5.5%
A 942202
 
5.4%
C 851797
 
4.9%
Other values (18) 4568772
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17500366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1740521
 
9.9%
_ 1614935
 
9.2%
D 1532749
 
8.8%
T 1465021
 
8.4%
N 1338780
 
7.7%
I 1261875
 
7.2%
O 1227170
 
7.0%
S 956544
 
5.5%
A 942202
 
5.4%
C 851797
 
4.9%
Other values (18) 4568772
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17500366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1740521
 
9.9%
_ 1614935
 
9.2%
D 1532749
 
8.8%
T 1465021
 
8.4%
N 1338780
 
7.7%
I 1261875
 
7.2%
O 1227170
 
7.0%
S 956544
 
5.5%
A 942202
 
5.4%
C 851797
 
4.9%
Other values (18) 4568772
26.1%

mediaType
Text

Missing 

Distinct19
Distinct (%)0.1%
Missing324090
Missing (%)95.9%
Memory size2.6 MiB
2025-01-07T12:10:20.466485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length362
Median length10
Mean length13.91095401
Min length10

Characters and Unicode

Total characters194809
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowStillImage
2nd rowStillImage
3rd rowStillImage
4th rowStillImage
5th rowStillImage
ValueCountFrequency (%)
stillimage 11788
84.2%
stillimage;stillimage 1471
 
10.5%
stillimage;stillimage;stillimage 245
 
1.7%
stillimage;stillimage;stillimage;stillimage 193
 
1.4%
stillimage;stillimage;stillimage;stillimage;stillimage 97
 
0.7%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 75
 
0.5%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 45
 
0.3%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 18
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 16
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 12
 
0.1%
Other values (9) 44
 
0.3%
2025-01-07T12:10:20.611616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 37966
19.5%
S 18983
9.7%
t 18983
9.7%
i 18983
9.7%
I 18983
9.7%
m 18983
9.7%
a 18983
9.7%
g 18983
9.7%
e 18983
9.7%
; 4979
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 37966
19.5%
S 18983
9.7%
t 18983
9.7%
i 18983
9.7%
I 18983
9.7%
m 18983
9.7%
a 18983
9.7%
g 18983
9.7%
e 18983
9.7%
; 4979
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 37966
19.5%
S 18983
9.7%
t 18983
9.7%
i 18983
9.7%
I 18983
9.7%
m 18983
9.7%
a 18983
9.7%
g 18983
9.7%
e 18983
9.7%
; 4979
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 37966
19.5%
S 18983
9.7%
t 18983
9.7%
i 18983
9.7%
I 18983
9.7%
m 18983
9.7%
a 18983
9.7%
g 18983
9.7%
e 18983
9.7%
; 4979
 
2.6%
Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
True
264632 
False
73457 
(Missing)
 
5
ValueCountFrequency (%)
True 264632
78.3%
False 73457
 
21.7%
(Missing) 5
 
< 0.1%
2025-01-07T12:10:20.679689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

hasGeospatialIssues
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
False
335533 
True
 
2556
(Missing)
 
5
ValueCountFrequency (%)
False 335533
99.2%
True 2556
 
0.8%
(Missing) 5
 
< 0.1%
2025-01-07T12:10:20.724969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

taxonKey
Real number (ℝ)

Zeros 

Distinct45746
Distinct (%)13.5%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3472928.423
Minimum0
Maximum12386548
Zeros6107
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:20.780525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1470
Q11887014
median2418036
Q35127354
95-th percentile9358186
Maximum12386548
Range12386548
Interquartile range (IQR)3240340

Descriptive statistics

Standard deviation2677935.935
Coefficient of variation (CV)0.7710887209
Kurtosis0.9477112845
Mean3472928.423
Median Absolute Deviation (MAD)668430
Skewness1.225851331
Sum1.174158898 × 1012
Variance7.171340874 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:20.850862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6107
 
1.8%
6841 3252
 
1.0%
637 2297
 
0.7%
2285664 2008
 
0.6%
2329589 2006
 
0.6%
2440447 1919
 
0.6%
8324617 1660
 
0.5%
7971837 1474
 
0.4%
2431491 1334
 
0.4%
2307333 1035
 
0.3%
Other values (45736) 314997
93.2%
ValueCountFrequency (%)
0 6107
1.8%
1 236
 
0.1%
3 3
 
< 0.1%
5 9
 
< 0.1%
6 335
 
0.1%
ValueCountFrequency (%)
12386548 6
< 0.1%
12385823 1
 
< 0.1%
12382748 2
 
< 0.1%
12374069 3
< 0.1%
12373983 1
 
< 0.1%

acceptedTaxonKey
Real number (ℝ)

Missing 

Distinct44951
Distinct (%)13.5%
Missing6112
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean3675126.507
Minimum1
Maximum12386548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:20.918365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5220
Q11941299.25
median2431198
Q35209252
95-th percentile9512501
Maximum12386548
Range12386547
Interquartile range (IQR)3267952.75

Descriptive statistics

Standard deviation2798762.119
Coefficient of variation (CV)0.7615417084
Kurtosis0.5025133424
Mean3675126.507
Median Absolute Deviation (MAD)685144
Skewness1.146371142
Sum1.220075848 × 1012
Variance7.833069397 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:20.986455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6841 3252
 
1.0%
637 2297
 
0.7%
2285664 2008
 
0.6%
2329589 2006
 
0.6%
2440447 1919
 
0.6%
8324617 1660
 
0.5%
8770992 1474
 
0.4%
2431491 1334
 
0.4%
2307333 1035
 
0.3%
68 875
 
0.3%
Other values (44941) 314122
92.9%
(Missing) 6112
 
1.8%
ValueCountFrequency (%)
1 236
0.1%
3 3
 
< 0.1%
5 9
 
< 0.1%
6 335
0.1%
19 24
 
< 0.1%
ValueCountFrequency (%)
12386548 6
< 0.1%
12385823 1
 
< 0.1%
12373983 1
 
< 0.1%
12356386 6
< 0.1%
12350721 5
< 0.1%

kingdomKey
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.545054705
Minimum0
Maximum6
Zeros6107
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.039507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.567851615
Coefficient of variation (CV)1.01475476
Kurtosis3.959161072
Mean1.545054705
Median Absolute Deviation (MAD)0
Skewness2.404784208
Sum522366
Variance2.458158687
MonotonicityNot monotonic
2025-01-07T12:10:21.087543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 291926
86.3%
6 35530
 
10.5%
0 6107
 
1.8%
4 3038
 
0.9%
3 1166
 
0.3%
5 322
 
0.1%
(Missing) 5
 
< 0.1%
ValueCountFrequency (%)
0 6107
 
1.8%
1 291926
86.3%
3 1166
 
0.3%
4 3038
 
0.9%
5 322
 
0.1%
ValueCountFrequency (%)
6 35530
 
10.5%
5 322
 
0.1%
4 3038
 
0.9%
3 1166
 
0.3%
1 291926
86.3%

phylumKey
Real number (ℝ)

Missing 

Distinct40
Distinct (%)< 0.1%
Missing6812
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean780928.8223
Minimum9
Maximum8770992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.148110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile43
Q144
median54
Q354
95-th percentile7707728
Maximum8770992
Range8770983
Interquartile range (IQR)10

Descriptive statistics

Standard deviation2335251.029
Coefficient of variation (CV)2.990350673
Kurtosis5.116104968
Mean780928.8223
Median Absolute Deviation (MAD)2
Skewness2.663194427
Sum2.587076621 × 1011
Variance5.453397369 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:21.357222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
54 145971
43.2%
44 103372
30.6%
7707728 30584
 
9.0%
52 20737
 
6.1%
42 11327
 
3.4%
43 3177
 
0.9%
106 2942
 
0.9%
8770992 2110
 
0.6%
50 1630
 
0.5%
36 1622
 
0.5%
Other values (30) 7810
 
2.3%
(Missing) 6812
 
2.0%
ValueCountFrequency (%)
9 4
 
< 0.1%
14 1
 
< 0.1%
19 58
 
< 0.1%
22 161
< 0.1%
34 271
0.1%
ValueCountFrequency (%)
8770992 2110
 
0.6%
8376456 70
 
< 0.1%
8173593 38
 
< 0.1%
7765738 1
 
< 0.1%
7707728 30584
9.0%

classKey
Real number (ℝ)

Missing 

Distinct105
Distinct (%)< 0.1%
Missing52277
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean609133.0678
Minimum119
Maximum12259753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.427366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile131
Q1216
median216
Q3229
95-th percentile7228684
Maximum12259753
Range12259634
Interquartile range (IQR)13

Descriptive statistics

Standard deviation2470204.914
Coefficient of variation (CV)4.055279617
Kurtosis13.79149817
Mean609133.0678
Median Absolute Deviation (MAD)9
Skewness3.926407297
Sum1.74100586 × 1011
Variance6.101912316 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:21.498601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216 112951
33.4%
229 27895
 
8.3%
359 24478
 
7.2%
131 18384
 
5.4%
220 15795
 
4.7%
196 10876
 
3.2%
256 10686
 
3.2%
137 9771
 
2.9%
225 9525
 
2.8%
11592253 9481
 
2.8%
Other values (95) 35975
 
10.6%
(Missing) 52277
15.5%
ValueCountFrequency (%)
119 3
 
< 0.1%
120 75
 
< 0.1%
121 724
 
0.2%
126 4
 
< 0.1%
131 18384
5.4%
ValueCountFrequency (%)
12259753 6
 
< 0.1%
12217645 2
 
< 0.1%
12203163 2
 
< 0.1%
11881065 30
 
< 0.1%
11592253 9481
2.8%

orderKey
Real number (ℝ)

Missing 

Distinct531
Distinct (%)0.2%
Missing30347
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean616655.3817
Minimum370
Maximum12228399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.571182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile537
Q1690
median797
Q31110
95-th percentile8349555
Maximum12228399
Range12228029
Interquartile range (IQR)420

Descriptive statistics

Standard deviation2279872.116
Coefficient of variation (CV)3.697157576
Kurtosis10.59801403
Mean616655.3817
Median Absolute Deviation (MAD)160
Skewness3.508527149
Sum1.897738438 × 1011
Variance5.197816866 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:21.644722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
797 79519
23.5%
587 25783
 
7.6%
637 23755
 
7.0%
1470 10132
 
3.0%
952 10009
 
3.0%
1457 8496
 
2.5%
1459 8406
 
2.5%
953 8204
 
2.4%
1369 7858
 
2.3%
733 7808
 
2.3%
Other values (521) 117777
34.8%
(Missing) 30347
 
9.0%
ValueCountFrequency (%)
370 4
 
< 0.1%
377 1
 
< 0.1%
381 1
 
< 0.1%
392 2629
0.8%
393 41
 
< 0.1%
ValueCountFrequency (%)
12228399 2
 
< 0.1%
12210182 1
 
< 0.1%
12204028 298
0.1%
12202578 250
0.1%
11919486 27
 
< 0.1%

familyKey
Real number (ℝ)

Missing 

Distinct3094
Distinct (%)1.0%
Missing19910
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean529105.6818
Minimum1889
Maximum12242766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.716229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1889
5-th percentile3032
Q14479
median6683
Q38535
95-th percentile4532185
Maximum12242766
Range12240877
Interquartile range (IQR)4056

Descriptive statistics

Standard deviation1664664.085
Coefficient of variation (CV)3.146184482
Kurtosis11.93728278
Mean529105.6818
Median Absolute Deviation (MAD)2115
Skewness3.413481235
Sum1.683529623 × 1011
Variance2.771106517 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:21.786986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4479 12102
 
3.6%
6950 12012
 
3.6%
7015 7500
 
2.2%
5343 7246
 
2.1%
6748 6784
 
2.0%
3073 6677
 
2.0%
5314 5540
 
1.6%
4532185 5452
 
1.6%
5854277 5009
 
1.5%
6841 4930
 
1.5%
Other values (3084) 244932
72.4%
(Missing) 19910
 
5.9%
ValueCountFrequency (%)
1889 2
 
< 0.1%
1895 17
< 0.1%
1897 10
 
< 0.1%
1905 1
 
< 0.1%
1952 35
< 0.1%
ValueCountFrequency (%)
12242766 37
< 0.1%
12241089 1
 
< 0.1%
12240677 3
 
< 0.1%
12222529 6
 
< 0.1%
12215321 10
 
< 0.1%

genusKey
Real number (ℝ)

Missing 

Distinct19382
Distinct (%)6.4%
Missing34396
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean2905090.032
Minimum1003628
Maximum12385823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.857108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1003628
5-th percentile1472040
Q11948434
median2387984
Q32693312
95-th percentile7822511
Maximum12385823
Range11382195
Interquartile range (IQR)744878

Descriptive statistics

Standard deviation1818446.586
Coefficient of variation (CV)0.6259518866
Kurtosis6.190063292
Mean2905090.032
Median Absolute Deviation (MAD)405617
Skewness2.522560333
Sum8.822700324 × 1011
Variance3.306747986 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:21.926517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2431477 4671
 
1.4%
4646327 4236
 
1.3%
2227127 3675
 
1.1%
2285664 2587
 
0.8%
2329589 2006
 
0.6%
2440446 1919
 
0.6%
2227317 1707
 
0.5%
2440326 1662
 
0.5%
4312471 1424
 
0.4%
8782549 1420
 
0.4%
Other values (19372) 278391
82.3%
(Missing) 34396
 
10.2%
ValueCountFrequency (%)
1003628 6
 
< 0.1%
1003709 7
< 0.1%
1003720 5
 
< 0.1%
1003734 16
< 0.1%
1003806 1
 
< 0.1%
ValueCountFrequency (%)
12385823 1
 
< 0.1%
12375178 2
 
< 0.1%
12373983 1
 
< 0.1%
12350721 5
< 0.1%
12344008 7
< 0.1%

subgenusKey
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

speciesKey
Real number (ℝ)

Missing 

Distinct37032
Distinct (%)14.9%
Missing89520
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean4085324.331
Minimum1003721
Maximum12386548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-01-07T12:10:21.992290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1003721
5-th percentile1735121
Q12227289
median2440447
Q35220003
95-th percentile9774759
Maximum12386548
Range11382827
Interquartile range (IQR)2992714

Descriptive statistics

Standard deviation2755665.074
Coefficient of variation (CV)0.6745278591
Kurtosis0.3266191164
Mean4085324.331
Median Absolute Deviation (MAD)641293
Skewness1.204711958
Sum1.01550541 × 1012
Variance7.593690002 × 1012
MonotonicityNot monotonic
2025-01-07T12:10:22.064353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2440447 1919
 
0.6%
8324617 1660
 
0.5%
2431491 1334
 
0.4%
2431423 815
 
0.2%
2432006 764
 
0.2%
2431513 639
 
0.2%
5218985 601
 
0.2%
4312492 579
 
0.2%
9001095 579
 
0.2%
2431543 566
 
0.2%
Other values (37022) 239118
70.7%
(Missing) 89520
 
26.5%
ValueCountFrequency (%)
1003721 5
< 0.1%
1004077 2
 
< 0.1%
1004095 2
 
< 0.1%
1004241 1
 
< 0.1%
1004257 2
 
< 0.1%
ValueCountFrequency (%)
12386548 6
 
< 0.1%
12356386 6
 
< 0.1%
12302386 3
 
< 0.1%
12297784 15
< 0.1%
12290919 1
 
< 0.1%

species
Text

Missing 

Distinct37025
Distinct (%)14.9%
Missing89520
Missing (%)26.5%
Memory size2.6 MiB
2025-01-07T12:10:22.277272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length30
Mean length19.21962474
Min length8

Characters and Unicode

Total characters4777499
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7675 ?
Unique (%)3.1%

Sample

1st rowRectiostoma fernaldella
2nd rowMesontoplatys bolzi
3rd rowDulcerana granularis
4th rowAmanses scopas
5th rowCalyptogena extenta
ValueCountFrequency (%)
plethodon 4564
 
0.9%
faxonius 4236
 
0.9%
procambarus 3408
 
0.7%
truncatus 1929
 
0.4%
tursiops 1919
 
0.4%
cinereus 1885
 
0.4%
delphis 1660
 
0.3%
delphinus 1660
 
0.3%
cambarus 1583
 
0.3%
anolis 1411
 
0.3%
Other values (38206) 472895
95.1%
2025-01-07T12:10:22.573030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 541195
 
11.3%
i 417200
 
8.7%
s 361384
 
7.6%
e 336874
 
7.1%
o 316429
 
6.6%
r 303496
 
6.4%
l 260561
 
5.5%
248576
 
5.2%
n 248318
 
5.2%
u 245629
 
5.1%
Other values (44) 1497837
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4777499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 541195
 
11.3%
i 417200
 
8.7%
s 361384
 
7.6%
e 336874
 
7.1%
o 316429
 
6.6%
r 303496
 
6.4%
l 260561
 
5.5%
248576
 
5.2%
n 248318
 
5.2%
u 245629
 
5.1%
Other values (44) 1497837
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4777499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 541195
 
11.3%
i 417200
 
8.7%
s 361384
 
7.6%
e 336874
 
7.1%
o 316429
 
6.6%
r 303496
 
6.4%
l 260561
 
5.5%
248576
 
5.2%
n 248318
 
5.2%
u 245629
 
5.1%
Other values (44) 1497837
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4777499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 541195
 
11.3%
i 417200
 
8.7%
s 361384
 
7.6%
e 336874
 
7.1%
o 316429
 
6.6%
r 303496
 
6.4%
l 260561
 
5.5%
248576
 
5.2%
n 248318
 
5.2%
u 245629
 
5.1%
Other values (44) 1497837
31.4%
Distinct44951
Distinct (%)13.5%
Missing6112
Missing (%)1.8%
Memory size2.6 MiB
2025-01-07T12:10:22.798161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length90
Mean length31.13227826
Min length5

Characters and Unicode

Total characters10335356
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9417 ?
Unique (%)2.8%

Sample

1st rowRectiostoma fernaldella
2nd rowSiboglinidae
3rd rowAmphinomidae
4th rowCambaridae
5th rowPolystichum Roth
ValueCountFrequency (%)
37558
 
3.0%
linnaeus 10225
 
0.8%
1758 8032
 
0.6%
l 6309
 
0.5%
1985 5095
 
0.4%
plethodon 4673
 
0.4%
walker 4511
 
0.4%
jones 4350
 
0.3%
faxonius 4236
 
0.3%
procambarus 3675
 
0.3%
Other values (49762) 1162095
92.9%
2025-01-07T12:10:23.098922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
918777
 
8.9%
a 834408
 
8.1%
e 661550
 
6.4%
i 629479
 
6.1%
r 530385
 
5.1%
s 523738
 
5.1%
o 514156
 
5.0%
n 463991
 
4.5%
l 422188
 
4.1%
t 361951
 
3.5%
Other values (97) 4474733
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10335356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
918777
 
8.9%
a 834408
 
8.1%
e 661550
 
6.4%
i 629479
 
6.1%
r 530385
 
5.1%
s 523738
 
5.1%
o 514156
 
5.0%
n 463991
 
4.5%
l 422188
 
4.1%
t 361951
 
3.5%
Other values (97) 4474733
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10335356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
918777
 
8.9%
a 834408
 
8.1%
e 661550
 
6.4%
i 629479
 
6.1%
r 530385
 
5.1%
s 523738
 
5.1%
o 514156
 
5.0%
n 463991
 
4.5%
l 422188
 
4.1%
t 361951
 
3.5%
Other values (97) 4474733
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10335356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
918777
 
8.9%
a 834408
 
8.1%
e 661550
 
6.4%
i 629479
 
6.1%
r 530385
 
5.1%
s 523738
 
5.1%
o 514156
 
5.0%
n 463991
 
4.5%
l 422188
 
4.1%
t 361951
 
3.5%
Other values (97) 4474733
43.3%
Distinct46008
Distinct (%)14.6%
Missing24039
Missing (%)7.1%
Memory size2.6 MiB
2025-01-07T12:10:23.327517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length85
Median length63
Mean length18.57454904
Min length3

Characters and Unicode

Total characters5833430
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10055 ?
Unique (%)3.2%

Sample

1st rowRectiostoma fernaldella
2nd rowPolystichum sp.
3rd rowMesontoplatys bolzi
4th rowBursa granularis
5th rowAmanses scopas
ValueCountFrequency (%)
sp 50633
 
7.9%
plethodon 4673
 
0.7%
orconectes 4548
 
0.7%
indet 4202
 
0.7%
procambarus 3784
 
0.6%
unidentified 3701
 
0.6%
bathymodiolus 2598
 
0.4%
cinereus 2325
 
0.4%
riftia 2006
 
0.3%
truncatus 1926
 
0.3%
Other values (42984) 556915
87.4%
2025-01-07T12:10:23.635113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 627231
 
10.8%
i 493887
 
8.5%
s 465451
 
8.0%
e 411782
 
7.1%
o 369786
 
6.3%
r 352399
 
6.0%
323256
 
5.5%
l 300021
 
5.1%
n 294984
 
5.1%
t 291085
 
5.0%
Other values (69) 1903548
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5833430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 627231
 
10.8%
i 493887
 
8.5%
s 465451
 
8.0%
e 411782
 
7.1%
o 369786
 
6.3%
r 352399
 
6.0%
323256
 
5.5%
l 300021
 
5.1%
n 294984
 
5.1%
t 291085
 
5.0%
Other values (69) 1903548
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5833430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 627231
 
10.8%
i 493887
 
8.5%
s 465451
 
8.0%
e 411782
 
7.1%
o 369786
 
6.3%
r 352399
 
6.0%
323256
 
5.5%
l 300021
 
5.1%
n 294984
 
5.1%
t 291085
 
5.0%
Other values (69) 1903548
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5833430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 627231
 
10.8%
i 493887
 
8.5%
s 465451
 
8.0%
e 411782
 
7.1%
o 369786
 
6.3%
r 352399
 
6.0%
323256
 
5.5%
l 300021
 
5.1%
n 294984
 
5.1%
t 291085
 
5.0%
Other values (69) 1903548
32.6%

typifiedName
Text

Missing 

Distinct13
Distinct (%)38.2%
Missing338060
Missing (%)> 99.9%
Memory size2.6 MiB
2025-01-07T12:10:23.716881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length9.352941176
Min length5

Characters and Unicode

Total characters318
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJaponica
2nd rowPallidata
3rd rowLepidophaga
4th rowDives
5th rowFurcifer
ValueCountFrequency (%)
lepidophaga 4
11.8%
dives 4
11.8%
tartarella 4
11.8%
inexpectata 4
11.8%
japonica 2
 
5.9%
furcifer 2
 
5.9%
pallidata 2
 
5.9%
pervada 2
 
5.9%
echinopanicis 2
 
5.9%
ruptifascia 2
 
5.9%
Other values (3) 6
17.6%
2025-01-07T12:10:23.877042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 56
17.6%
i 28
 
8.8%
e 28
 
8.8%
t 20
 
6.3%
r 20
 
6.3%
p 18
 
5.7%
l 18
 
5.7%
o 16
 
5.0%
n 14
 
4.4%
c 14
 
4.4%
Other values (19) 86
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 56
17.6%
i 28
 
8.8%
e 28
 
8.8%
t 20
 
6.3%
r 20
 
6.3%
p 18
 
5.7%
l 18
 
5.7%
o 16
 
5.0%
n 14
 
4.4%
c 14
 
4.4%
Other values (19) 86
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 56
17.6%
i 28
 
8.8%
e 28
 
8.8%
t 20
 
6.3%
r 20
 
6.3%
p 18
 
5.7%
l 18
 
5.7%
o 16
 
5.0%
n 14
 
4.4%
c 14
 
4.4%
Other values (19) 86
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 56
17.6%
i 28
 
8.8%
e 28
 
8.8%
t 20
 
6.3%
r 20
 
6.3%
p 18
 
5.7%
l 18
 
5.7%
o 16
 
5.0%
n 14
 
4.4%
c 14
 
4.4%
Other values (19) 86
27.0%

protocol
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:23.940950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1014267
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
5th rowEML
ValueCountFrequency (%)
eml 338089
100.0%
2025-01-07T12:10:24.073178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 338089
33.3%
M 338089
33.3%
L 338089
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1014267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 338089
33.3%
M 338089
33.3%
L 338089
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1014267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 338089
33.3%
M 338089
33.3%
L 338089
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1014267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 338089
33.3%
M 338089
33.3%
L 338089
33.3%
Distinct31887
Distinct (%)9.4%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:24.315146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99607204
Min length20

Characters and Unicode

Total characters8112808
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3500 ?
Unique (%)1.0%

Sample

1st row2024-12-01T12:07:01.240Z
2nd row2024-12-01T12:07:01.438Z
3rd row2024-12-01T12:07:01.443Z
4th row2024-12-01T12:07:01.449Z
5th row2024-12-01T12:07:01.465Z
ValueCountFrequency (%)
2024-12-01t12:07:38.532z 73
 
< 0.1%
2024-12-01t12:07:38.533z 71
 
< 0.1%
2024-12-01t12:07:38.508z 68
 
< 0.1%
2024-12-01t12:07:39.879z 67
 
< 0.1%
2024-12-01t12:07:40.339z 65
 
< 0.1%
2024-12-01t12:07:37.936z 65
 
< 0.1%
2024-12-01t12:07:39.819z 65
 
< 0.1%
2024-12-01t12:07:39.875z 64
 
< 0.1%
2024-12-01t12:07:39.723z 64
 
< 0.1%
2024-12-01t12:07:38.854z 63
 
< 0.1%
Other values (31877) 337424
99.8%
2025-01-07T12:10:24.508096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (5) 866870
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8112808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (5) 866870
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8112808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (5) 866870
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8112808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1562001
19.3%
1 1172147
14.4%
0 1166550
14.4%
: 676178
8.3%
- 676178
8.3%
4 503489
 
6.2%
7 475460
 
5.9%
T 338089
 
4.2%
Z 338089
 
4.2%
. 337757
 
4.2%
Other values (5) 866870
10.7%

lastCrawled
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:24.584511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters8114136
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-12-01T11:07:21.711Z
2nd row2024-12-01T11:07:21.711Z
3rd row2024-12-01T11:07:21.711Z
4th row2024-12-01T11:07:21.711Z
5th row2024-12-01T11:07:21.711Z
ValueCountFrequency (%)
2024-12-01t11:07:21.711z 338089
100.0%
2025-01-07T12:10:24.746920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2366623
29.2%
2 1352356
16.7%
0 1014267
12.5%
- 676178
 
8.3%
7 676178
 
8.3%
: 676178
 
8.3%
T 338089
 
4.2%
4 338089
 
4.2%
. 338089
 
4.2%
Z 338089
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8114136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2366623
29.2%
2 1352356
16.7%
0 1014267
12.5%
- 676178
 
8.3%
7 676178
 
8.3%
: 676178
 
8.3%
T 338089
 
4.2%
4 338089
 
4.2%
. 338089
 
4.2%
Z 338089
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8114136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2366623
29.2%
2 1352356
16.7%
0 1014267
12.5%
- 676178
 
8.3%
7 676178
 
8.3%
: 676178
 
8.3%
T 338089
 
4.2%
4 338089
 
4.2%
. 338089
 
4.2%
Z 338089
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8114136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2366623
29.2%
2 1352356
16.7%
0 1014267
12.5%
- 676178
 
8.3%
7 676178
 
8.3%
: 676178
 
8.3%
T 338089
 
4.2%
4 338089
 
4.2%
. 338089
 
4.2%
Z 338089
 
4.2%

repatriated
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing10837
Missing (%)3.2%
Memory size2.6 MiB
True
176301 
False
150956 
(Missing)
 
10837
ValueCountFrequency (%)
True 176301
52.1%
False 150956
44.6%
(Missing) 10837
 
3.2%
2025-01-07T12:10:24.805092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

relativeOrganismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB

projectId
Unsupported

Missing  Rejected  Unsupported 

Missing338094
Missing (%)100.0%
Memory size2.6 MiB
Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
False
266778 
True
71311 
(Missing)
 
5
ValueCountFrequency (%)
False 266778
78.9%
True 71311
 
21.1%
(Missing) 5
 
< 0.1%
2025-01-07T12:10:24.855156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

gbifRegion
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing12220
Missing (%)3.6%
Memory size2.6 MiB
2025-01-07T12:10:24.899691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length10.89375035
Min length4

Characters and Unicode

Total characters3549990
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowLATIN_AMERICA
3rd rowOCEANIA
4th rowNORTH_AMERICA
5th rowASIA
ValueCountFrequency (%)
north_america 153734
47.2%
latin_america 78014
23.9%
oceania 37070
 
11.4%
asia 32944
 
10.1%
africa 17920
 
5.5%
europe 5860
 
1.8%
antarctica 332
 
0.1%
2025-01-07T12:10:25.016241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 718374
20.2%
R 409594
11.5%
I 398028
11.2%
C 287402
8.1%
E 280538
 
7.9%
N 269150
 
7.6%
T 232412
 
6.5%
M 231748
 
6.5%
_ 231748
 
6.5%
O 196664
 
5.5%
Other values (6) 294332
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3549990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 718374
20.2%
R 409594
11.5%
I 398028
11.2%
C 287402
8.1%
E 280538
 
7.9%
N 269150
 
7.6%
T 232412
 
6.5%
M 231748
 
6.5%
_ 231748
 
6.5%
O 196664
 
5.5%
Other values (6) 294332
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3549990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 718374
20.2%
R 409594
11.5%
I 398028
11.2%
C 287402
8.1%
E 280538
 
7.9%
N 269150
 
7.6%
T 232412
 
6.5%
M 231748
 
6.5%
_ 231748
 
6.5%
O 196664
 
5.5%
Other values (6) 294332
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3549990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 718374
20.2%
R 409594
11.5%
I 398028
11.2%
C 287402
8.1%
E 280538
 
7.9%
N 269150
 
7.6%
T 232412
 
6.5%
M 231748
 
6.5%
_ 231748
 
6.5%
O 196664
 
5.5%
Other values (6) 294332
8.3%

publishedByGbifRegion
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size2.6 MiB
2025-01-07T12:10:25.069438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters4395157
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 338089
100.0%
2025-01-07T12:10:25.191422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 676178
15.4%
A 676178
15.4%
N 338089
7.7%
O 338089
7.7%
T 338089
7.7%
H 338089
7.7%
_ 338089
7.7%
M 338089
7.7%
E 338089
7.7%
I 338089
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4395157
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 676178
15.4%
A 676178
15.4%
N 338089
7.7%
O 338089
7.7%
T 338089
7.7%
H 338089
7.7%
_ 338089
7.7%
M 338089
7.7%
E 338089
7.7%
I 338089
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4395157
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 676178
15.4%
A 676178
15.4%
N 338089
7.7%
O 338089
7.7%
T 338089
7.7%
H 338089
7.7%
_ 338089
7.7%
M 338089
7.7%
E 338089
7.7%
I 338089
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4395157
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 676178
15.4%
A 676178
15.4%
N 338089
7.7%
O 338089
7.7%
T 338089
7.7%
H 338089
7.7%
_ 338089
7.7%
M 338089
7.7%
E 338089
7.7%
I 338089
7.7%

level0Gid
Text

Missing 

Distinct174
Distinct (%)0.1%
Missing157741
Missing (%)46.7%
Memory size2.6 MiB
2025-01-07T12:10:25.362360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters541059
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowUSA
2nd rowCHN
3rd rowKEN
4th rowDJI
5th rowUSA
ValueCountFrequency (%)
usa 97215
53.9%
mmr 7455
 
4.1%
mex 4807
 
2.7%
guy 4333
 
2.4%
phl 4062
 
2.3%
pyf 3614
 
2.0%
chn 3325
 
1.8%
bra 2654
 
1.5%
sur 2611
 
1.4%
mdg 2411
 
1.3%
Other values (164) 47866
26.5%
2025-01-07T12:10:25.591941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 113189
20.9%
U 111068
20.5%
S 106325
19.7%
M 27389
 
5.1%
R 20032
 
3.7%
P 16725
 
3.1%
G 16411
 
3.0%
N 16119
 
3.0%
C 13892
 
2.6%
E 11267
 
2.1%
Other values (18) 88642
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 541059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 113189
20.9%
U 111068
20.5%
S 106325
19.7%
M 27389
 
5.1%
R 20032
 
3.7%
P 16725
 
3.1%
G 16411
 
3.0%
N 16119
 
3.0%
C 13892
 
2.6%
E 11267
 
2.1%
Other values (18) 88642
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 541059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 113189
20.9%
U 111068
20.5%
S 106325
19.7%
M 27389
 
5.1%
R 20032
 
3.7%
P 16725
 
3.1%
G 16411
 
3.0%
N 16119
 
3.0%
C 13892
 
2.6%
E 11267
 
2.1%
Other values (18) 88642
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 541059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 113189
20.9%
U 111068
20.5%
S 106325
19.7%
M 27389
 
5.1%
R 20032
 
3.7%
P 16725
 
3.1%
G 16411
 
3.0%
N 16119
 
3.0%
C 13892
 
2.6%
E 11267
 
2.1%
Other values (18) 88642
16.4%

level0Name
Text

Missing 

Distinct174
Distinct (%)0.1%
Missing157741
Missing (%)46.7%
Memory size2.6 MiB
2025-01-07T12:10:25.798990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length13
Mean length11.06804988
Min length4

Characters and Unicode

Total characters1996156
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowChina
3rd rowKenya
4th rowDjibouti
5th rowUnited States
ValueCountFrequency (%)
united 97300
32.1%
states 97245
32.1%
myanmar 7455
 
2.5%
méxico 4807
 
1.6%
guyana 4333
 
1.4%
philippines 4062
 
1.3%
french 3933
 
1.3%
polynesia 3614
 
1.2%
china 3325
 
1.1%
new 2794
 
0.9%
Other values (202) 73833
24.4%
2025-01-07T12:10:26.078111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 308073
15.4%
e 235889
11.8%
a 215937
10.8%
i 164095
8.2%
n 156657
7.8%
122348
 
6.1%
s 118233
 
5.9%
d 109469
 
5.5%
S 105440
 
5.3%
U 97741
 
4.9%
Other values (51) 362274
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1996156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 308073
15.4%
e 235889
11.8%
a 215937
10.8%
i 164095
8.2%
n 156657
7.8%
122348
 
6.1%
s 118233
 
5.9%
d 109469
 
5.5%
S 105440
 
5.3%
U 97741
 
4.9%
Other values (51) 362274
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1996156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 308073
15.4%
e 235889
11.8%
a 215937
10.8%
i 164095
8.2%
n 156657
7.8%
122348
 
6.1%
s 118233
 
5.9%
d 109469
 
5.5%
S 105440
 
5.3%
U 97741
 
4.9%
Other values (51) 362274
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1996156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 308073
15.4%
e 235889
11.8%
a 215937
10.8%
i 164095
8.2%
n 156657
7.8%
122348
 
6.1%
s 118233
 
5.9%
d 109469
 
5.5%
S 105440
 
5.3%
U 97741
 
4.9%
Other values (51) 362274
18.1%

level1Gid
Text

Missing 

Distinct1235
Distinct (%)0.7%
Missing158980
Missing (%)47.0%
Memory size2.6 MiB
2025-01-07T12:10:26.298599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.591483636
Min length6

Characters and Unicode

Total characters1359741
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st rowUSA.3_1
2nd rowCHN.29_1
3rd rowKEN.20_1
4th rowDJI.3_1
5th rowUSA.2_1
ValueCountFrequency (%)
usa.5_1 10272
 
5.7%
usa.44_1 9642
 
5.4%
usa.3_1 9251
 
5.2%
usa.10_1 8853
 
4.9%
usa.47_1 6219
 
3.5%
mmr.14_1 5452
 
3.0%
usa.21_1 4649
 
2.6%
usa.34_1 4328
 
2.4%
usa.43_1 3079
 
1.7%
usa.32_1 3055
 
1.7%
Other values (1225) 114314
63.8%
2025-01-07T12:10:26.587380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 235844
17.3%
_ 179114
13.2%
. 179079
13.2%
A 113189
8.3%
U 109841
8.1%
S 106325
 
7.8%
4 56352
 
4.1%
2 42387
 
3.1%
3 40233
 
3.0%
M 27389
 
2.0%
Other values (28) 269988
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1359741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 235844
17.3%
_ 179114
13.2%
. 179079
13.2%
A 113189
8.3%
U 109841
8.1%
S 106325
 
7.8%
4 56352
 
4.1%
2 42387
 
3.1%
3 40233
 
3.0%
M 27389
 
2.0%
Other values (28) 269988
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1359741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 235844
17.3%
_ 179114
13.2%
. 179079
13.2%
A 113189
8.3%
U 109841
8.1%
S 106325
 
7.8%
4 56352
 
4.1%
2 42387
 
3.1%
3 40233
 
3.0%
M 27389
 
2.0%
Other values (28) 269988
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1359741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 235844
17.3%
_ 179114
13.2%
. 179079
13.2%
A 113189
8.3%
U 109841
8.1%
S 106325
 
7.8%
4 56352
 
4.1%
2 42387
 
3.1%
3 40233
 
3.0%
M 27389
 
2.0%
Other values (28) 269988
19.9%

level1Name
Text

Missing 

Distinct1201
Distinct (%)0.7%
Missing158980
Missing (%)47.0%
Memory size2.6 MiB
2025-01-07T12:10:26.790933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length9.053652981
Min length3

Characters and Unicode

Total characters1621636
Distinct characters101
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowArizona
2nd rowXizang
3rd rowLaikipia
4th rowDjiboutii
5th rowAlaska
ValueCountFrequency (%)
california 10397
 
4.5%
texas 9642
 
4.2%
arizona 9251
 
4.0%
florida 8853
 
3.9%
virginia 8047
 
3.5%
new 6247
 
2.7%
carolina 6031
 
2.6%
tanintharyi 5452
 
2.4%
maryland 4649
 
2.0%
north 4454
 
1.9%
Other values (1333) 155634
68.1%
2025-01-07T12:10:27.061203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 229700
14.2%
i 163412
 
10.1%
n 127711
 
7.9%
r 114166
 
7.0%
o 112430
 
6.9%
e 91904
 
5.7%
s 74858
 
4.6%
l 66916
 
4.1%
t 53967
 
3.3%
49543
 
3.1%
Other values (91) 537029
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1621636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 229700
14.2%
i 163412
 
10.1%
n 127711
 
7.9%
r 114166
 
7.0%
o 112430
 
6.9%
e 91904
 
5.7%
s 74858
 
4.6%
l 66916
 
4.1%
t 53967
 
3.3%
49543
 
3.1%
Other values (91) 537029
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1621636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 229700
14.2%
i 163412
 
10.1%
n 127711
 
7.9%
r 114166
 
7.0%
o 112430
 
6.9%
e 91904
 
5.7%
s 74858
 
4.6%
l 66916
 
4.1%
t 53967
 
3.3%
49543
 
3.1%
Other values (91) 537029
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1621636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 229700
14.2%
i 163412
 
10.1%
n 127711
 
7.9%
r 114166
 
7.0%
o 112430
 
6.9%
e 91904
 
5.7%
s 74858
 
4.6%
l 66916
 
4.1%
t 53967
 
3.3%
49543
 
3.1%
Other values (91) 537029
33.1%

level2Gid
Text

Missing 

Distinct4284
Distinct (%)2.5%
Missing167237
Missing (%)49.5%
Memory size2.6 MiB
2025-01-07T12:10:27.285907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.21143412
Min length8

Characters and Unicode

Total characters1744695
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique290 ?
Unique (%)0.2%

Sample

1st rowUSA.3.2_1
2nd rowCHN.29.7_1
3rd rowKEN.20.2_1
4th rowDJI.3.1_2
5th rowUSA.2.9_1
ValueCountFrequency (%)
mmr.14.2_1 3998
 
2.3%
usa.3.2_1 3339
 
2.0%
usa.3.11_1 2680
 
1.6%
usa.9.1_1 2296
 
1.3%
guy.2.8_1 2193
 
1.3%
usa.5.37_1 1794
 
1.1%
usa.26.95_1 1453
 
0.9%
usa.32.26_1 1409
 
0.8%
usa.44.22_1 1372
 
0.8%
mmr.14.3_1 1320
 
0.8%
Other values (4274) 149003
87.2%
2025-01-07T12:10:27.576642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 341679
19.6%
1 278548
16.0%
_ 170857
9.8%
A 112729
 
6.5%
U 109431
 
6.3%
S 105468
 
6.0%
2 95569
 
5.5%
4 80788
 
4.6%
3 72057
 
4.1%
5 47041
 
2.7%
Other values (28) 330528
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1744695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 341679
19.6%
1 278548
16.0%
_ 170857
9.8%
A 112729
 
6.5%
U 109431
 
6.3%
S 105468
 
6.0%
2 95569
 
5.5%
4 80788
 
4.6%
3 72057
 
4.1%
5 47041
 
2.7%
Other values (28) 330528
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1744695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 341679
19.6%
1 278548
16.0%
_ 170857
9.8%
A 112729
 
6.5%
U 109431
 
6.3%
S 105468
 
6.0%
2 95569
 
5.5%
4 80788
 
4.6%
3 72057
 
4.1%
5 47041
 
2.7%
Other values (28) 330528
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1744695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 341679
19.6%
1 278548
16.0%
_ 170857
9.8%
A 112729
 
6.5%
U 109431
 
6.3%
S 105468
 
6.0%
2 95569
 
5.5%
4 80788
 
4.6%
3 72057
 
4.1%
5 47041
 
2.7%
Other values (28) 330528
18.9%

level2Name
Text

Missing 

Distinct3706
Distinct (%)2.2%
Missing167249
Missing (%)49.5%
Memory size2.6 MiB
2025-01-07T12:10:27.783138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length8.680839357
Min length2

Characters and Unicode

Total characters1483078
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)0.1%

Sample

1st rowCochise
2nd rowShigatse
3rd rowLaikipia North
4th rowDjiboutii
5th rowHaines
ValueCountFrequency (%)
of 6496
 
2.8%
san 5277
 
2.3%
kawthoung 3998
 
1.7%
region 3757
 
1.6%
rest 3754
 
1.6%
cochise 3339
 
1.5%
saint 2993
 
1.3%
city 2937
 
1.3%
pima 2680
 
1.2%
columbia 2348
 
1.0%
Other values (3993) 191864
83.6%
2025-01-07T12:10:28.054770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 169779
 
11.4%
o 113886
 
7.7%
e 111771
 
7.5%
n 109067
 
7.4%
i 102293
 
6.9%
r 76609
 
5.2%
t 66878
 
4.5%
58598
 
4.0%
s 57490
 
3.9%
l 54814
 
3.7%
Other values (121) 561893
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1483078
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 169779
 
11.4%
o 113886
 
7.7%
e 111771
 
7.5%
n 109067
 
7.4%
i 102293
 
6.9%
r 76609
 
5.2%
t 66878
 
4.5%
58598
 
4.0%
s 57490
 
3.9%
l 54814
 
3.7%
Other values (121) 561893
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1483078
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 169779
 
11.4%
o 113886
 
7.7%
e 111771
 
7.5%
n 109067
 
7.4%
i 102293
 
6.9%
r 76609
 
5.2%
t 66878
 
4.5%
58598
 
4.0%
s 57490
 
3.9%
l 54814
 
3.7%
Other values (121) 561893
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1483078
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 169779
 
11.4%
o 113886
 
7.7%
e 111771
 
7.5%
n 109067
 
7.4%
i 102293
 
6.9%
r 76609
 
5.2%
t 66878
 
4.5%
58598
 
4.0%
s 57490
 
3.9%
l 54814
 
3.7%
Other values (121) 561893
37.9%

level3Gid
Text

Missing 

Distinct1717
Distinct (%)4.5%
Missing300258
Missing (%)88.8%
Memory size2.6 MiB
2025-01-07T12:10:28.264463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.93075378
Min length11

Characters and Unicode

Total characters451412
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.3%

Sample

1st rowCHN.29.7.10_1
2nd rowKEN.20.2.3_1
3rd rowPAN.12.6.1_1
4th rowPAN.3.3.1_1
5th rowCHN.29.5.1_1
ValueCountFrequency (%)
mmr.14.2.1_1 3995
 
10.6%
mdg.3.5.1_1 907
 
2.4%
pan.3.3.1_1 740
 
2.0%
mmr.14.3.3_1 720
 
1.9%
cri.4.10.3_1 708
 
1.9%
mmr.14.3.1_1 600
 
1.6%
chn.29.5.5_1 570
 
1.5%
ken.20.2.3_1 553
 
1.5%
mdg.6.2.3_1 539
 
1.4%
bol.8.14.1_2 531
 
1.4%
Other values (1707) 27973
73.9%
2025-01-07T12:10:28.546024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 113508
25.1%
1 78511
17.4%
_ 37836
 
8.4%
2 27108
 
6.0%
3 18840
 
4.2%
M 18153
 
4.0%
4 17781
 
3.9%
R 12466
 
2.8%
5 11565
 
2.6%
C 9801
 
2.2%
Other values (24) 105843
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 451412
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 113508
25.1%
1 78511
17.4%
_ 37836
 
8.4%
2 27108
 
6.0%
3 18840
 
4.2%
M 18153
 
4.0%
4 17781
 
3.9%
R 12466
 
2.8%
5 11565
 
2.6%
C 9801
 
2.2%
Other values (24) 105843
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 451412
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 113508
25.1%
1 78511
17.4%
_ 37836
 
8.4%
2 27108
 
6.0%
3 18840
 
4.2%
M 18153
 
4.0%
4 17781
 
3.9%
R 12466
 
2.8%
5 11565
 
2.6%
C 9801
 
2.2%
Other values (24) 105843
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 451412
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 113508
25.1%
1 78511
17.4%
_ 37836
 
8.4%
2 27108
 
6.0%
3 18840
 
4.2%
M 18153
 
4.0%
4 17781
 
3.9%
R 12466
 
2.8%
5 11565
 
2.6%
C 9801
 
2.2%
Other values (24) 105843
23.4%

level3Name
Text

Missing 

Distinct1653
Distinct (%)4.4%
Missing300562
Missing (%)88.9%
Memory size2.6 MiB
2025-01-07T12:10:28.746088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length8.898300117
Min length3

Characters and Unicode

Total characters333971
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)0.3%

Sample

1st rowNyalam
2nd rowSegera
3rd rowAncón
4th rowEl Harino
5th rowBomi
ValueCountFrequency (%)
bokpyin 3995
 
8.0%
el 1132
 
2.3%
ifanadiana 907
 
1.8%
san 907
 
1.8%
las 754
 
1.5%
harino 740
 
1.5%
tenasserim 720
 
1.4%
horquetas 708
 
1.4%
poblacion 702
 
1.4%
mergui 600
 
1.2%
Other values (1879) 39014
77.7%
2025-01-07T12:10:29.001316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 46937
 
14.1%
n 29594
 
8.9%
o 26066
 
7.8%
i 24210
 
7.2%
r 16414
 
4.9%
e 15331
 
4.6%
12647
 
3.8%
u 10324
 
3.1%
g 9540
 
2.9%
s 9502
 
2.8%
Other values (101) 133406
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 46937
 
14.1%
n 29594
 
8.9%
o 26066
 
7.8%
i 24210
 
7.2%
r 16414
 
4.9%
e 15331
 
4.6%
12647
 
3.8%
u 10324
 
3.1%
g 9540
 
2.9%
s 9502
 
2.8%
Other values (101) 133406
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 46937
 
14.1%
n 29594
 
8.9%
o 26066
 
7.8%
i 24210
 
7.2%
r 16414
 
4.9%
e 15331
 
4.6%
12647
 
3.8%
u 10324
 
3.1%
g 9540
 
2.9%
s 9502
 
2.8%
Other values (101) 133406
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 46937
 
14.1%
n 29594
 
8.9%
o 26066
 
7.8%
i 24210
 
7.2%
r 16414
 
4.9%
e 15331
 
4.6%
12647
 
3.8%
u 10324
 
3.1%
g 9540
 
2.9%
s 9502
 
2.8%
Other values (101) 133406
39.9%

iucnRedListCategory
Text

Missing 

Distinct9
Distinct (%)< 0.1%
Missing63456
Missing (%)18.8%
Memory size2.6 MiB
2025-01-07T12:10:29.065375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters549276
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNE
2nd rowNE
3rd rowNE
4th rowNE
5th rowNE
ValueCountFrequency (%)
ne 174813
63.7%
lc 87132
31.7%
vu 3277
 
1.2%
nt 3158
 
1.1%
dd 2671
 
1.0%
en 2568
 
0.9%
cr 968
 
0.4%
ex 34
 
< 0.1%
ew 17
 
< 0.1%
2025-01-07T12:10:29.178865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 180539
32.9%
E 177432
32.3%
C 88100
16.0%
L 87132
15.9%
D 5342
 
1.0%
U 3277
 
0.6%
V 3277
 
0.6%
T 3158
 
0.6%
R 968
 
0.2%
X 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 549276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 180539
32.9%
E 177432
32.3%
C 88100
16.0%
L 87132
15.9%
D 5342
 
1.0%
U 3277
 
0.6%
V 3277
 
0.6%
T 3158
 
0.6%
R 968
 
0.2%
X 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 549276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 180539
32.9%
E 177432
32.3%
C 88100
16.0%
L 87132
15.9%
D 5342
 
1.0%
U 3277
 
0.6%
V 3277
 
0.6%
T 3158
 
0.6%
R 968
 
0.2%
X 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 549276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 180539
32.9%
E 177432
32.3%
C 88100
16.0%
L 87132
15.9%
D 5342
 
1.0%
U 3277
 
0.6%
V 3277
 
0.6%
T 3158
 
0.6%
R 968
 
0.2%
X 34
 
< 0.1%